diff --git a/COPYRIGHT b/COPYRIGHT
index fbb6592..35bb170 100644
--- a/COPYRIGHT
+++ b/COPYRIGHT
@@ -1,5 +1,5 @@
 
-Copyright (c) 2015 The LIBFFM Project.
+Copyright (c) 2017 The LIBFFM Project.
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
diff --git a/Makefile b/Makefile
index 61584a7..0e9b5b4 100644
--- a/Makefile
+++ b/Makefile
@@ -1,20 +1,26 @@
 CXX = g++
 CXXFLAGS = -Wall -O3 -std=c++0x -march=native
 
+# comment the following flags if you do not want to SSE instructions
+DFLAG += -DUSESSE
+
 # comment the following flags if you do not want to use OpenMP
 DFLAG += -DUSEOMP
 CXXFLAGS += -fopenmp
 
 all: ffm-train ffm-predict
 
-ffm-train: ffm-train.cpp ffm.o
-	$(CXX) $(CXXFLAGS) -o $@ $^
+ffm-train: ffm-train.cpp ffm.o timer.o
+	$(CXX) $(CXXFLAGS) $(DFLAG) -o $@ $^
+
+ffm-predict: ffm-predict.cpp ffm.o timer.o
+	$(CXX) $(CXXFLAGS) $(DFLAG) -o $@ $^
 
-ffm-predict: ffm-predict.cpp ffm.o
-	$(CXX) $(CXXFLAGS) -o $@ $^
+ffm.o: ffm.cpp ffm.h timer.o
+	$(CXX) $(CXXFLAGS) $(DFLAG) -c -o $@ $<
 
-ffm.o: ffm.cpp ffm.h
+timer.o: timer.cpp timer.h
 	$(CXX) $(CXXFLAGS) $(DFLAG) -c -o $@ $<
 
 clean:
-	rm -f ffm-train ffm-predict ffm.o
+	rm -f ffm-train ffm-predict ffm.o timer.o
diff --git a/README b/README
index 914c95d..b6a3a6d 100644
--- a/README
+++ b/README
@@ -1,21 +1,40 @@
-LIBFFM is a library for field-aware factorization machine. For the formulation it solves, please check:
-
-    http://www.csie.ntu.edu.tw/~r01922136/slides/ffm.pdf
-
-
-
 Table of Contents
 =================
 
+- What is LIBFFM
 - Overfitting and Early Stopping
 - Installation
 - Data Format
 - Command Line Usage
 - Examples
-- Library Usage
-- OpenMP
+- OpenMP and SSE
 - Building Windows Binaries
+- FAQ
+
+
+What is LIBFFM
+==============
+
+LIBFFM is a library for field-aware factorization machine (FFM). 
+
+Field-aware factorization machine is a effective model for CTR prediction. It has been used to win the top-3 positions
+of following competitions:
+
+    * Criteo: https://www.kaggle.com/c/criteo-display-ad-challenge
+
+    * Avazu: https://www.kaggle.com/c/avazu-ctr-prediction
+
+    * Outbrain: https://www.kaggle.com/c/outbrain-click-prediction
 
+    * RecSys 2015: http://dl.acm.org/citation.cfm?id=2813511&dl=ACM&coll=DL&CFID=941880276&CFTOKEN=60022934
+
+You can find more information about FFM in the following paper / slides:
+
+    * http://www.csie.ntu.edu.tw/~r01922136/slides/ffm.pdf
+
+    * http://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf
+
+    * https://arxiv.org/abs/1701.04099
 
 
 Overfitting and Early Stopping
@@ -78,16 +97,11 @@ stop at the iteration that reaches the best validation loss:
 Installation
 ============
 
-Requirement: LIBFFM is written in C++. It requires C++11 and OpenMP supports. If OpenMP is not available on your
-platform, please refer to section `OpenMP.' 
+Requirement: It requires a C++11 compatible compiler. We also use OpenMP to provide multi-threading. If OpenMP is not
+available on your platform, please refer to section `OpenMP and SSE.'
 
 - Unix-like systems:
-  To compile on Unix-like systems, type `make' in the command line.
-
-- OS X:
-  The built-in compiler should be able to compile LIBFFM. However, OpenMP may
-  not be supported. In this case you have to compile without OpenMP. See
-  section `OpenMP' for detail.
+  Typeype `make' in the command line.
 
 - Windows:
   See `Building Windows Binaries' to compile.
@@ -99,14 +113,42 @@ Data Format
 
 The data format of LIBFFM is:
 
-<label> <field1>:<index1>:<value1> <field2>:<index2>:<value2> ...
+<label> <field1>:<feature1>:<value1> <field2>:<feature2>:<value2> ...
 .
 .
 .
 
-`field' and `index' should be non-negative integers. See an example
-`bigdata.tr.txt.'
+`field' and `feature' should be non-negative integers. See an example `bigdata.tr.txt.'
+
+It is important to understand the difference between `field' and `feature'. For example, if we have a raw data like this:
+
+Click  Advertiser  Publisher
+=====  ==========  =========
+    0        Nike        CNN
+    1        ESPN        BBC
+
+Here, we have 
+
+    * 2 fields: Advertiser and Publisher
+
+    * 4 features: Advertiser-Nike, Advertiser-ESPN, Publisher-CNN, Publisher-BBC
 
+Usually you will need to build two dictionares, one for field and one for features, like this:
+    
+    DictField[Advertiser] -> 0
+    DictField[Publisher]  -> 1
+    
+    DictFeature[Advertiser-Nike] -> 0
+    DictFeature[Publisher-CNN]   -> 1
+    DictFeature[Advertiser-ESPN] -> 2
+    DictFeature[Publisher-BBC]   -> 3
+
+Then, you can generate FFM format data:
+
+    0 0:0:1 1:1:1
+    1 0:2:1 1:3:1
+
+Note that because these features are categorical, the values here are all ones.
 
 
 Command Line Usage
@@ -123,27 +165,13 @@ Command Line Usage
     -r <eta>: set learning rate (default 0.2)
     -s <nr_threads>: set number of threads (default 1)
     -p <path>: set path to the validation set
-    -v <fold>: set the number of folds for cross-validation
     --quiet: quiet model (no output)
     --no-norm: disable instance-wise normalization
-    --no-rand: disable random update
-    --on-disk: perform on-disk training (a temporary file <training_set_file>.bin will be generated)
     --auto-stop: stop at the iteration that achieves the best validation loss (must be used with -p)
 
     By default we do instance-wise normalization. That is, we normalize the 2-norm of each instance to 1. You can use
     `--no-norm' to disable this function.
     
-    By default, our algorithm randomly select an instance for update in each inner iteration. On some datasets you may
-    want to do update in the original order. You can do it by using `--no-rand' together with `-s 1.'
-
-    If you do not have enough memory, then you can use `--on-disk' to do disk-level training. Two restrictions when you
-    use this mode:
-        
-        1. So far we do not allow random update in the mode, so please use
-           `--no-rand' if you want to do on-disk training. 
-           
-        2. Cross-validation in this mode is not yet supported.
-
     A binary file `training_set_file.bin' will be generated to store the data in binary format.
 
     Because FFM usually need early stopping for better test performance, we provide an option `--auto-stop' to stop at
@@ -160,157 +188,45 @@ Command Line Usage
 Examples
 ========
 
-> ffm-train bigdata.tr.txt model
+Download a toy data from:
 
-train a model using the default parameters
+    zip: https://www.csie.ntu.edu.tw/~f01922139/libffm_data/libffm_toy.zip
 
-> ffm-train -l 0.001 -k 16 -t 30 -r 0.05 -s 4 bigdata.tr.txt model
+    tar.gz: https://www.csie.ntu.edu.tw/~f01922139/libffm_data/libffm_toy.tar.gz
 
-train a model using the following parameters:
+This dataset is subsampled 1% from Criteo's challenge.
 
-    regularization cost = 0.001
-    latent factors = 16
-    iterations = 30
-    learning rate = 0.05
-    threads = 4
+> tar -xzf libffm_toy.tar.gz
 
-> ffm-train -p bigdata.te.txt bigdata.tr.txt model
+or 
 
-use bigdata.te.txt as validation set
+> unzip libffm_toy.zip
 
-> ffm-train -v 5 bigdata.tr.txt
 
-do five fold cross validation
+> ./ffm-train -p libffm_toy/criteo.va.r100.gbdt0.ffm libffm_toy/criteo.tr.r100.gbdt0.ffm model
 
-> ffm-train --quiet bigdata.tr.txt
+train a model using the default parameters
 
-do not print message to screen
 
-> ffm-predict bigdata.te.txt model output
+> ./ffm-predict libffm_toy/criteo.va.r100.gbdt0.ffm model output
 
 do prediction
 
-> ffm-train --no-rand --on-disk bigdata.tr.txt
-
-perform on-disk training
-
-> ffm-train -p bigdata.te.txt -t 100 --auto-stop bigdata.tr.txt
-
-use auto-stop to stop at the best iteration according to validation loss
-
-Library Usage
-=============
-
-These structures and functions are declared in the header file `ffm.h.' You need to #include `ffm.h' in your C/C++
-source files and link your program with `ffm.cpp.' You can see `ffm-train.cpp' and `ffm-predict.cpp' for examples
-showing how to use them.
-
-There are four public data structures in LIBFFM.
-
-
--   struct ffm_node
-    {
-        ffm_int f;    // field index
-        ffm_int j;    // column index
-        ffm_float v;  // value
-    };
-
-    Each `ffm_node' represents a non-zero element in a sparse matrix.
 
--   struct ffm_problem
-    {
-        ffm_int n;      // number of features
-        ffm_int l;      // number of instances
-        ffm_int m;      // number of fields
-        ffm_node *X;    // non-zero elements
-        ffm_long *P;    // row pointers
-        ffm_float *Y;   // labels
-    };
+> ./ffm-train -l 0.0001 -k 15 -t 30 -r 0.05 -s 4 --auto-stop -p libffm_toy/criteo.va.r100.gbdt0.ffm libffm_toy/criteo.tr.r100.gbdt0.ffm model
 
--   struct ffm_parameter
-    {
-        ffm_float eta;
-        ffm_float lambda;
-        ffm_int nr_iters;
-        ffm_int k;
-        ffm_int nr_threads;
-        bool quiet;
-        bool normalization;
-        bool random;
-        bool auto_stop;
-    };
-
-    `ffm_parameter' represents the parameters used for training. The meaning of
-    each variable is:
-
-    variable         meaning                             default
-    ============================================================
-    eta              learning rate                           0.1
-    lambda           regularization cost                       0
-    nr_iters         number of iterations                     15
-    k                number of latent factors                  4
-    nr_threads       number of threads used                    1
-    quiet            no outputs to stdout                  false
-    normalization    instance-wise normalization           false
-    random           randomly select instance in SG         true
-    auto_stop        auto stop at the best iteration       false
-
-    To obtain a parameter object with default values, use the function
-    `ffm_get_default_param.'
-
-
--   struct ffm_model
-    {
-        ffm_int n;              // number of features
-        ffm_int m;              // number of fields
-        ffm_int k;              // number of latent factors
-        ffm_float *W;           // store model values
-        bool normalization;     // do instance-wise normalization
-    };
-
-
-
-Functions available in LIBFFM include:
-
-
--   ffm_parameter ffm_get_default_param();
-
-    Get default parameters.
-
--   ffm_int ffm_save_model(struct ffm_model const *model, char const *path);
-    
-    Save a model. It returns 0 on sucess and 1 on failure.
-
--   struct ffm_model* ffm_load_model(char const *path);
-
-    Load a model. If the model could not be loaded, a nullptr is returned.
-
--   void ffm_destroy_model(struct ffm_model **model);
-    
-    Destroy a model.
-
--   struct ffm_model* ffm_train(struct ffm_problem const *prob, ffm_parameter param);
-
-    Train a model.
-
--   struct ffm_model* ffm_train_with_validation(struct ffm_problem const *Tr, struct ffm_problem const *Va, ffm_parameter param);
-
-    Train a model with training set `Tr' and validation set `Va.' The logloss of the validation set is printed at each
-    iteration.
-    
--   ffm_float ffm_cross_validation(struct ffm_problem const *prob, ffm_int nr_folds, ffm_parameter param);
-
-    Do cross validation with `nr_folds' folds.
-
--   ffm_float ffm_predict(ffm_node *begin, ffm_node *end, ffm_model *model);
-
-    Do prediction. `begin' and `end' are pointers to specify the beginning and ending position of the instance to be
-    predicted.
+train a model using the following parameters:
 
+    regularization cost = 0.0001
+    latent factors = 15
+    iterations = 30
+    learning rate = 0.3
+    threads = 4
+    let it auto-stop
 
 
-OpenMP
-======
+OpenMP and SSE
+==============
 
 We use OpenMP to do parallelization. If OpenMP is not available on your
 platform, then please comment out the following lines in Makefile.
@@ -318,18 +234,27 @@ platform, then please comment out the following lines in Makefile.
     DFLAG += -DUSEOMP
     CXXFLAGS += -fopenmp
 
-Note: Please always run `make clean all' if these flags are changed.
+Note: Please run `make clean all' if these flags are changed.
+
+We use SSE instructions to perform fast computation. If you do not want to use it, comment out the following line:
+
+    DFLAG += -DUSESSE
+
+Then, run `make clean all'
 
 
 
 Building Windows Binaries
 =========================
 
+The Windows part is maintained by different maintainer, so it may not always support the latest version.
+
+The latest version it supports is: v1.14
+
 To build them via command-line tools of Visual C++, use the following steps:
 
-1. Open a DOS command box (or Developer Command Prompt for Visual Studio) and
-go to LIBFFM directory. If environment variables of VC++ have not been set,
-type
+1. Open a DOS command box (or Developer Command Prompt for Visual Studio) and go to LIBFFM directory. If environment
+variables of VC++ have not been set, type
 
 "C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\amd64\vcvars64.bat"
 
@@ -341,12 +266,29 @@ where it is installed.
 nmake -f Makefile.win clean all
 
 
+FAQ
+===
+
+Q: Why I have the same model size when k = 1 and k = 4?
+
+A: This is because we use SSE instructions. In order to use SSE, the memory need to be aligned. So even you assign k =
+   1, we still fill some dummy zeros from k = 2 to 4.
+
+
+Q: Why the logloss is slightly different on the same data when I run the program two or more times when I use multi-threading
+
+A: When there are more then one thread, the program becomes non-deterministic. To make it determinisitc you can only use one thread.
+
 
 Contributors
 ============
 
 Yuchin Juan, Wei-Sheng Chin, and Yong Zhuang
 
-For questions, comments, feature requests, or bug report, please send your email to 
+For questions, comments, feature requests, or bug report, please send your email to:
+
+    Yuchin Juan (juan.yuchin@gmail.com)
+
+For Windows related questions, please send your email to:
 
-    Yuchin (guestwalk@gmail.com)
+    Wei-Sheng Chin (image.chin@gmail.com)
diff --git a/bigdata.te.txt b/bigdata.te.txt
deleted file mode 100644
index 6f966af..0000000
--- a/bigdata.te.txt
+++ /dev/null
@@ -1,200 +0,0 @@
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diff --git a/bigdata.tr.txt b/bigdata.tr.txt
deleted file mode 100644
index 7a00318..0000000
--- a/bigdata.tr.txt
+++ /dev/null
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-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:610:0.3651 6:5918:0.3651 7:2166:0.3651 8:8131:0.3651 9:5483:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-1 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:8786:0.3651 6:185:0.3651 7:4258:0.3651 8:8131:0.3651 9:5483:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-1 1:449:0.3651 2:439:0.3651 3:1243:0.3651 4:2183:0.3651 5:9200:0.3651 6:185:0.3651 7:1398:0.3651 8:4331:0.3651 9:5483:0.3651 10:5620:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:9083:0.50000 16:8226:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9882:0.3651 6:185:0.3651 7:4258:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:1988:0.3651 2:400:0.3651 3:1243:0.3651 4:2183:0.3651 5:3777:0.3651 6:185:0.3651 7:6677:0.3651 8:1343:0.3651 9:3899:0.3651 10:9493:0.3651 11:1520:0.3651 12:1232:0.3651 13:2738:0.3651 14:8813:0.3651 15:6145:0.3651 17:7148:0.50000 16:1828:0.50000
-1 1:2146:0.3651 2:4357:0.3651 3:9952:0.3651 4:9619:0.3651 5:21:0.3651 6:185:0.3651 7:7714:0.3651 8:7470:0.3651 9:4698:0.3651 10:8245:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:663:0.50000 16:2035:0.50000
-0 1:48:0.3651 2:8451:0.3651 3:9474:0.3651 4:2183:0.3651 5:8216:0.3651 6:185:0.3651 7:1969:0.3651 8:3240:0.3651 9:4861:0.3651 10:3387:0.3651 11:1520:0.3651 12:9714:0.3651 13:2549:0.3651 14:2935:0.3651 15:5428:0.3651 17:7026:0.50000 16:8965:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9774:0.3651 6:185:0.3651 7:4258:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:5980:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9653:0.3651 6:185:0.3651 7:2166:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:405:0.3651 13:2738:0.3651 14:2935:0.3651 15:6145:0.3651 17:2434:0.50000 16:7755:0.50000
-1 1:206:0.3651 2:674:0.3651 3:9952:0.3651 4:9619:0.3651 5:6963:0.3651 6:185:0.3651 7:7230:0.3651 8:653:0.3651 9:3899:0.3651 10:5620:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:5398:0.50000 16:8255:0.50000
-0 1:513:0.3651 2:8451:0.3651 3:9474:0.3651 4:2183:0.3651 5:4154:0.3651 6:185:0.3651 7:4692:0.3651 8:1786:0.3651 9:1861:0.3651 10:4062:0.3651 11:1520:0.3651 12:9714:0.3651 13:2549:0.3651 14:2935:0.3651 15:5428:0.3651 17:7026:0.50000 16:1418:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:2863:0.3651 6:185:0.3651 7:8355:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:563:0.3651 13:2738:0.3651 14:2935:0.3651 15:2757:0.3651 17:9083:0.12127 17:2434:0.48507 16:7755:0.48507 16:9111:0.12127
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9882:0.3651 6:185:0.3651 7:1693:0.3651 8:8131:0.3651 9:5483:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:5250:0.3651 2:6423:0.3651 3:6764:0.3651 4:9619:0.3651 5:8408:0.3651 6:185:0.3651 7:5525:0.3651 8:7752:0.3651 9:381:0.3651 10:9493:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:5316:0.50000 16:5065:0.50000
-1 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:4658:0.3651 6:185:0.3651 7:5599:0.3651 8:8131:0.3651 9:5483:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:3561:0.3651 6:185:0.3651 7:5671:0.3651 8:8131:0.3651 9:6160:0.3651 10:215:0.3651 11:1520:0.3651 12:267:0.3651 13:2738:0.3651 14:8813:0.3651 15:6145:0.3651 17:2434:0.50000 16:7755:0.50000
-1 1:7501:0.3651 2:439:0.3651 3:1243:0.3651 4:2183:0.3651 5:1624:0.3651 6:185:0.3651 7:1398:0.3651 8:4331:0.3651 9:5483:0.3651 10:5620:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:9083:0.50000 16:872:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:421:0.3651 6:185:0.3651 7:5599:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:143:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:6795:0.3651 6:185:0.3651 7:4258:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:143:0.3651 13:2738:0.3651 14:2935:0.3651 15:1929:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:1382:0.3651 2:9270:0.3651 3:9952:0.3651 4:2183:0.3651 5:3169:0.3651 6:185:0.3651 7:7196:0.3651 8:1343:0.3651 9:3899:0.3651 10:9493:0.3651 11:1520:0.3651 12:4999:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:8737:0.50000 16:232:0.50000
-0 1:6076:0.3651 2:1921:0.3651 3:8672:0.3651 4:2183:0.3651 5:1750:0.3651 6:185:0.3651 7:6704:0.3651 8:8580:0.3651 9:3899:0.3651 10:6805:0.3651 11:1520:0.3651 12:1089:0.3651 13:2738:0.3651 14:2935:0.3651 15:1929:0.3651 17:2004:0.28868 17:8616:0.28868 17:9990:0.28868 16:2198:0.28868 16:1762:0.28868 16:2803:0.28868
-0 1:1988:0.3651 2:400:0.3651 3:1243:0.3651 4:2183:0.3651 5:573:0.3651 6:185:0.3651 7:7196:0.3651 8:1343:0.3651 9:3899:0.3651 10:9493:0.3651 11:1520:0.3651 12:7685:0.3651 13:2738:0.3651 14:9125:0.3651 15:6858:0.3651 17:7148:0.50000 16:1828:0.50000
-1 1:6381:0.3651 2:3648:0.3651 3:9952:0.3651 4:2183:0.3651 5:1501:0.3651 6:185:0.3651 7:3741:0.3651 8:7752:0.3651 9:3899:0.3651 10:9493:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:6323:0.50000 16:6611:0.50000
-0 1:1988:0.3651 2:400:0.3651 3:1243:0.3651 4:2183:0.3651 5:2310:0.3651 6:185:0.3651 7:867:0.3651 8:1343:0.3651 9:3899:0.3651 10:9493:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:7148:0.50000 16:1828:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:7305:0.3651 6:185:0.3651 7:2166:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:2850:0.3651 2:3395:0.3651 3:9952:0.3651 4:9619:0.3651 5:2694:0.3651 6:185:0.3651 7:3735:0.3651 8:2685:0.3651 9:3899:0.3651 10:7582:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:8574:0.50000 16:6848:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:2431:0.3651 6:185:0.3651 7:2166:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:9100:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:7633:0.3651 2:8195:0.3651 3:9952:0.3651 4:9619:0.3651 5:5383:0.3651 6:185:0.3651 7:932:0.3651 8:5166:0.3651 9:8193:0.3651 10:4465:0.3651 11:1520:0.3651 12:4999:0.3651 13:2738:0.3651 14:2935:0.3651 15:6145:0.3651 17:3723:0.50000 16:928:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9882:0.3651 6:185:0.3651 7:4258:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:1232:0.3651 13:2738:0.3651 14:8813:0.3651 15:6145:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:5116:0.3651 2:6465:0.3651 3:9952:0.3651 4:2183:0.3651 5:6506:0.3651 6:185:0.3651 7:3741:0.3651 8:7752:0.3651 9:3899:0.3651 10:9493:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:8795:0.50000 16:9899:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:3122:0.3651 6:185:0.3651 7:5671:0.3651 8:8131:0.3651 9:5483:0.3651 10:215:0.3651 11:1520:0.3651 12:9100:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9882:0.3651 6:185:0.3651 7:2569:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:7685:0.3651 13:2738:0.3651 14:2935:0.3651 15:6145:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:7111:0.3651 6:185:0.3651 7:1424:0.3651 8:8131:0.3651 9:5483:0.3651 10:215:0.3651 11:1520:0.3651 12:1232:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:249:0.3651 6:185:0.3651 7:2569:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:6187:0.3651 13:2738:0.3651 14:2935:0.3651 15:4252:0.3651 17:2867:0.03835 17:2434:0.49853 16:1725:0.03835 16:7755:0.49853
-0 1:2146:0.3651 2:4357:0.3651 3:9952:0.3651 4:9619:0.3651 5:2654:0.3651 6:185:0.3651 7:3076:0.3651 8:260:0.3651 9:6391:0.3651 10:953:0.3651 11:1520:0.3651 12:143:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:663:0.50000 16:2035:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:3561:0.3651 6:185:0.3651 7:9637:0.3651 8:8131:0.3651 9:6160:0.3651 10:215:0.3651 11:1520:0.3651 12:267:0.3651 13:2738:0.3651 14:8813:0.3651 15:6145:0.3651 17:2434:0.50000 16:7755:0.50000
-1 1:9551:0.3651 2:610:0.3651 3:9952:0.3651 4:9619:0.3651 5:3780:0.3651 6:185:0.3651 7:7424:0.3651 8:4515:0.3651 9:3899:0.3651 10:4465:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:8926:0.50000 16:4511:0.50000
-1 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9115:0.3651 6:185:0.3651 7:2569:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:3780:0.3651 6:185:0.3651 7:5671:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:4222:0.3651 2:1163:0.3651 3:8672:0.3651 4:2183:0.3651 5:9653:0.3651 6:185:0.3651 7:5671:0.3651 8:8131:0.3651 9:3899:0.3651 10:215:0.3651 11:1520:0.3651 12:7685:0.3651 13:2738:0.3651 14:2935:0.3651 15:6145:0.3651 17:2434:0.50000 16:7755:0.50000
-0 1:9551:0.3651 2:610:0.3651 3:9952:0.3651 4:9619:0.3651 5:7389:0.3651 6:185:0.3651 7:8644:0.3651 8:2285:0.3651 9:4861:0.3651 10:6908:0.3651 11:1520:0.3651 12:9714:0.3651 13:2738:0.3651 14:2935:0.3651 15:5428:0.3651 17:8926:0.50000 16:4511:0.50000
diff --git a/ffm-predict.cpp b/ffm-predict.cpp
index a1c990f..5fdecad 100644
--- a/ffm-predict.cpp
+++ b/ffm-predict.cpp
@@ -14,19 +14,16 @@
 using namespace std;
 using namespace ffm;
 
-struct Option
-{
+struct Option {
     string test_path, model_path, output_path;
 };
 
-string predict_help()
-{
+string predict_help() {
     return string(
 "usage: ffm-predict test_file model_file output_file\n");
 }
 
-Option parse_option(int argc, char **argv)
-{
+Option parse_option(int argc, char **argv) {
     vector<string> args;
     for(int i = 0; i < argc; i++)
         args.push_back(string(argv[i]));
@@ -46,28 +43,25 @@ Option parse_option(int argc, char **argv)
     return option;
 }
 
-void predict(string test_path, string model_path, string output_path)
-{
+void predict(string test_path, string model_path, string output_path) {
     int const kMaxLineSize = 1000000;
 
     FILE *f_in = fopen(test_path.c_str(), "r");
     ofstream f_out(output_path);
     char line[kMaxLineSize];
 
-    ffm_model *model = ffm_load_model(model_path.c_str());
+    ffm_model model = ffm_load_model(model_path);
 
     ffm_double loss = 0;
     vector<ffm_node> x;
     ffm_int i = 0;
 
-    for(; fgets(line, kMaxLineSize, f_in) != nullptr; i++)
-    {
+    for(; fgets(line, kMaxLineSize, f_in) != nullptr; i++) {
         x.clear();
         char *y_char = strtok(line, " \t");
         ffm_float y = (atoi(y_char)>0)? 1.0f : -1.0f;
 
-        while(true)
-        {
+        while(true) {
             char *field_char = strtok(nullptr,":");
             char *idx_char = strtok(nullptr,":");
             char *value_char = strtok(nullptr," \t");
@@ -92,23 +86,18 @@ void predict(string test_path, string model_path, string output_path)
     loss /= i;
 
     cout << "logloss = " << fixed << setprecision(5) << loss << endl;
-
-    ffm_destroy_model(&model);
-
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char **argv) {
     Option option;
-    try
-    {
+    try {
         option = parse_option(argc, argv);
-    }
-    catch(invalid_argument const &e)
-    {
+    } catch(invalid_argument const &e) {
         cout << e.what() << endl;
         return 1;
     }
 
     predict(option.test_path, option.model_path, option.output_path);
+
+    return 0;
 }
diff --git a/ffm-train.cpp b/ffm-train.cpp
index fb362a7..55fdc11 100644
--- a/ffm-train.cpp
+++ b/ffm-train.cpp
@@ -9,11 +9,14 @@
 
 #include "ffm.h"
 
+#if defined USEOMP
+#include <omp.h>
+#endif
+
 using namespace std;
 using namespace ffm;
 
-string train_help()
-{
+string train_help() {
     return string(
 "usage: ffm-train [options] training_set_file [model_file]\n"
 "\n"
@@ -24,25 +27,21 @@ string train_help()
 "-r <eta>: set learning rate (default 0.2)\n"
 "-s <nr_threads>: set number of threads (default 1)\n"
 "-p <path>: set path to the validation set\n"
-"-v <fold>: set the number of folds for cross-validation\n"
 "--quiet: quiet model (no output)\n"
 "--no-norm: disable instance-wise normalization\n"
-"--no-rand: disable random update\n"
-"--on-disk: perform on-disk training (a temporary file <training_set_file>.bin will be generated)\n"
 "--auto-stop: stop at the iteration that achieves the best validation loss (must be used with -p)\n");
 }
 
-struct Option
-{
-    Option() : param(ffm_get_default_param()), nr_folds(1), do_cv(false), on_disk(false) {}
-    string tr_path, va_path, model_path;
+struct Option {
+    string tr_path;
+    string va_path;
+    string model_path;
     ffm_parameter param;
-    ffm_int nr_folds;
-    bool do_cv, on_disk;
+    bool quiet = false;
+    ffm_int nr_threads = 1;
 };
 
-string basename(string path)
-{
+string basename(string path) {
     const char *ptr = strrchr(&*path.begin(), '/');
     if(!ptr)
         ptr = path.c_str();
@@ -51,8 +50,7 @@ string basename(string path)
     return string(ptr);
 }
 
-Option parse_option(int argc, char **argv)
-{
+Option parse_option(int argc, char **argv) {
     vector<string> args;
     for(int i = 0; i < argc; i++)
         args.push_back(string(argv[i]));
@@ -63,8 +61,7 @@ Option parse_option(int argc, char **argv)
     Option opt;
 
     ffm_int i = 1;
-    for(; i < argc; i++)
-    {
+    for(; i < argc; i++) {
         if(args[i].compare("-t") == 0)
         {
             if(i == argc-1)
@@ -73,82 +70,46 @@ Option parse_option(int argc, char **argv)
             opt.param.nr_iters = atoi(args[i].c_str());
             if(opt.param.nr_iters <= 0)
                 throw invalid_argument("number of iterations should be greater than zero");
-        }
-        else if(args[i].compare("-k") == 0)
-        {
+        } else if(args[i].compare("-k") == 0) {
             if(i == argc-1)
                 throw invalid_argument("need to specify number of factors after -k");
             i++;
             opt.param.k = atoi(args[i].c_str());
             if(opt.param.k <= 0)
                 throw invalid_argument("number of factors should be greater than zero");
-        }
-        else if(args[i].compare("-r") == 0)
-        {
+        } else if(args[i].compare("-r") == 0) {
             if(i == argc-1)
                 throw invalid_argument("need to specify eta after -r");
             i++;
             opt.param.eta = atof(args[i].c_str());
             if(opt.param.eta <= 0)
                 throw invalid_argument("learning rate should be greater than zero");
-        }
-        else if(args[i].compare("-l") == 0)
-        {
+        } else if(args[i].compare("-l") == 0) {
             if(i == argc-1)
                 throw invalid_argument("need to specify lambda after -l");
             i++;
             opt.param.lambda = atof(args[i].c_str());
             if(opt.param.lambda < 0)
                 throw invalid_argument("regularization cost should not be smaller than zero");
-        }
-        else if(args[i].compare("-s") == 0)
-        {
+        } else if(args[i].compare("-s") == 0) {
             if(i == argc-1)
                 throw invalid_argument("need to specify number of threads after -s");
             i++;
-            opt.param.nr_threads = atoi(args[i].c_str());
-            if(opt.param.nr_threads <= 0)
+            opt.nr_threads = atoi(args[i].c_str());
+            if(opt.nr_threads <= 0)
                 throw invalid_argument("number of threads should be greater than zero");
-        }
-        else if(args[i].compare("-v") == 0)
-        {
-            if(i == argc-1)
-                throw invalid_argument("need to specify number of folds after -v");
-            i++;
-            opt.nr_folds = atoi(args[i].c_str());
-            if(opt.nr_folds <= 1)
-                throw invalid_argument("number of folds should be greater than one");
-            opt.do_cv = true;
-        }
-        else if(args[i].compare("-p") == 0)
-        {
+        } else if(args[i].compare("-p") == 0) {
             if(i == argc-1)
                 throw invalid_argument("need to specify path after -p");
             i++;
             opt.va_path = args[i];
-        }
-        else if(args[i].compare("--no-norm") == 0)
-        {
+        } else if(args[i].compare("--no-norm") == 0) {
             opt.param.normalization = false;
-        }
-        else if(args[i].compare("--quiet") == 0)
-        {
-            opt.param.quiet = true;
-        }
-        else if(args[i].compare("--no-rand") == 0)
-        {
-            opt.param.random = false;
-        }
-        else if(args[i].compare("--on-disk") == 0)
-        {
-            opt.on_disk = true;
-        }
-        else if(args[i].compare("--auto-stop") == 0)
-        {
+        } else if(args[i].compare("--quiet") == 0) {
+            opt.quiet = true;
+        } else if(args[i].compare("--auto-stop") == 0) {
             opt.param.auto_stop = true;
-        }
-        else
-        {
+        } else {
             break;
         }
     }
@@ -159,118 +120,54 @@ Option parse_option(int argc, char **argv)
     opt.tr_path = args[i];
     i++;
 
-    if(i < argc)
-    {
+    if(i < argc) {
         opt.model_path = string(args[i]);
-    }
-    else if(i == argc)
-    {
+    } else if(i == argc) {
         opt.model_path = basename(opt.tr_path) + ".model";
-    }
-    else
-    {
+    } else {
         throw invalid_argument("cannot parse argument");
     }
 
     return opt;
 }
 
-int train(Option opt)
-{
-    ffm_problem *tr = ffm_read_problem(opt.tr_path.c_str());
-    if(tr == nullptr)
-    {
-        cerr << "cannot load " << opt.tr_path << endl << flush;
-        return 1;
-    }
-
-    ffm_problem *va = nullptr;
-    if(!opt.va_path.empty())
-    {
-        va = ffm_read_problem(opt.va_path.c_str());
-        if(va == nullptr)
-        {
-            ffm_destroy_problem(&tr);
-            cerr << "cannot load " << opt.va_path << endl << flush;
-            return 1;
-        }
-    }
-
-    int status = 0;
-    if(opt.do_cv)
-    {
-        ffm_cross_validation(tr, opt.nr_folds, opt.param);
-    }
-    else
-    {
-        ffm_model *model = ffm_train_with_validation(tr, va, opt.param);
-
-        status = ffm_save_model(model, opt.model_path.c_str());
-
-        ffm_destroy_model(&model);
-    }
-
-    ffm_destroy_problem(&tr);
-    ffm_destroy_problem(&va);
-
-    return status;
-}
-
-int train_on_disk(Option opt)
-{
-    if(opt.param.random)
-    {
-        cout << "Random update is not allowed in disk-level training. Please use `--no-rand' to disable." << endl;
-        return 1;
-    }
-
-    if(opt.do_cv)
-    {
-        cout << "Cross-validation is not yet implemented in disk-level training." << endl;
-        return 1;
-    }
-
+int train_on_disk(Option opt) {
     string tr_bin_path = basename(opt.tr_path) + ".bin";
     string va_bin_path = opt.va_path.empty()? "" : basename(opt.va_path) + ".bin";
 
-    ffm_read_problem_to_disk(opt.tr_path.c_str(), tr_bin_path.c_str());
+    ffm_read_problem_to_disk(opt.tr_path, tr_bin_path);
     if(!opt.va_path.empty())
-        ffm_read_problem_to_disk(opt.va_path.c_str(), va_bin_path.c_str());
-
-    ffm_model *model = ffm_train_with_validation_on_disk(tr_bin_path.c_str(), va_bin_path.c_str(), opt.param);
+        ffm_read_problem_to_disk(opt.va_path, va_bin_path);
 
-    ffm_int status = ffm_save_model(model, opt.model_path.c_str());
-    if(status != 0)
-    {
-        ffm_destroy_model(&model);
+    ffm_model model = ffm_train_on_disk(tr_bin_path.c_str(), va_bin_path.c_str(), opt.param);
 
-        return 1;
-    }
-
-    ffm_destroy_model(&model);
+    ffm_save_model(model, opt.model_path);
 
     return 0;
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char **argv) {
     Option opt;
-    try
-    {
+    try {
         opt = parse_option(argc, argv);
-    }
-    catch(invalid_argument &e)
-    {
+    } catch(invalid_argument &e) {
         cout << e.what() << endl;
         return 1;
     }
 
-    if(opt.on_disk)
-    {
-        return train_on_disk(opt);
-    }
-    else
-    {
-        return train(opt);
+    if(opt.quiet)
+        cout.setstate(ios_base::badbit);
+
+    if(opt.param.auto_stop && opt.va_path.empty()) {
+        cout << "To use auto-stop, you need to assign a validation set" << endl;
+        return 1;
     }
+
+#if defined USEOMP
+    omp_set_num_threads(opt.nr_threads);
+#endif
+
+    train_on_disk(opt);
+
+    return 0;
 }
diff --git a/ffm.cpp b/ffm.cpp
index 6af5bae..2f13845 100644
--- a/ffm.cpp
+++ b/ffm.cpp
@@ -1,3 +1,23 @@
+/*
+The following table is the meaning of some variables in this code:
+
+W: The pointer to the beginning of the model
+w: Dynamic pointer to access values in the model
+m: Number of fields
+k: Number of latent factors
+n: Number of features
+l: Number of data points
+f: Field index (0 to m-1)
+d: Latent factor index (0 to k-1)
+j: Feature index (0 to n-1)
+i: Data point index (0 to l-1)
+nnz: Number of non-zero elements
+X, P: Used to store the problem in a compressed sparse row (CSR) format. len(X) = nnz, len(P) = l + 1
+Y: The label. len(Y) = l
+R: Precomputed scaling factor to make the 2-norm of each instance to be 1. len(R) = l
+v: Value of each element in the problem
+*/
+
 #pragma GCC diagnostic ignored "-Wunused-result" 
 #include <algorithm>
 #include <cmath>
@@ -11,13 +31,19 @@
 #include <string>
 #include <cstring>
 #include <vector>
+#include <cassert>
+#include <numeric>
+
+#if defined USESSE
 #include <pmmintrin.h>
+#endif
 
 #if defined USEOMP
 #include <omp.h>
 #endif
 
 #include "ffm.h"
+#include "timer.h"
 
 namespace ffm {
 
@@ -25,11 +51,26 @@ namespace {
 
 using namespace std;
 
+#if defined USESSE
 ffm_int const kALIGNByte = 16;
+#else
+ffm_int const kALIGNByte = 4;
+#endif
+
 ffm_int const kALIGN = kALIGNByte/sizeof(ffm_float);
 ffm_int const kCHUNK_SIZE = 10000000;
 ffm_int const kMaxLineSize = 100000;
 
+inline ffm_int get_k_aligned(ffm_int k) {
+    return (ffm_int) ceil((ffm_float)k / kALIGN) * kALIGN;
+}
+
+ffm_long get_w_size(ffm_model &model) {
+    ffm_int k_aligned = get_k_aligned(model.k);
+    return (ffm_long) model.n * model.m * k_aligned * 2;
+}
+
+#if defined USESSE
 inline ffm_float wTx(
     ffm_node *begin,
     ffm_node *end,
@@ -38,10 +79,10 @@ inline ffm_float wTx(
     ffm_float kappa=0, 
     ffm_float eta=0, 
     ffm_float lambda=0, 
-    bool do_update=false)
-{
-    ffm_long align0 = (ffm_long)model.k*2;
-    ffm_long align1 = (ffm_long)model.m*align0;
+    bool do_update=false) {
+
+    ffm_int align0 = 2 * get_k_aligned(model.k);
+    ffm_int align1 = model.m * align0;
 
     __m128 XMMkappa = _mm_set1_ps(kappa);
     __m128 XMMeta = _mm_set1_ps(eta);
@@ -65,8 +106,8 @@ inline ffm_float wTx(
             if(j2 >= model.n || f2 >= model.m)
                 continue;
 
-            ffm_float *w1 = model.W + j1*align1 + f2*align0;
-            ffm_float *w2 = model.W + j2*align1 + f1*align0;
+            ffm_float *w1_base = model.W + j1*align1 + f2*align0;
+            ffm_float *w2_base = model.W + j2*align1 + f1*align0;
 
             __m128 XMMv = _mm_set1_ps(v1*v2*r);
 
@@ -74,15 +115,19 @@ inline ffm_float wTx(
             {
                 __m128 XMMkappav = _mm_mul_ps(XMMkappa, XMMv);
 
-                ffm_float *wg1 = w1 + model.k;
-                ffm_float *wg2 = w2 + model.k;
-                for(ffm_int d = 0; d < model.k; d += 4)
+                for(ffm_int d = 0; d < align0; d += kALIGN * 2)
                 {
-                    __m128 XMMw1 = _mm_load_ps(w1+d);
-                    __m128 XMMw2 = _mm_load_ps(w2+d);
+                    ffm_float *w1 = w1_base + d;
+                    ffm_float *w2 = w2_base + d;
+
+                    ffm_float *wg1 = w1 + kALIGN;
+                    ffm_float *wg2 = w2 + kALIGN;
 
-                    __m128 XMMwg1 = _mm_load_ps(wg1+d);
-                    __m128 XMMwg2 = _mm_load_ps(wg2+d);
+                    __m128 XMMw1 = _mm_load_ps(w1);
+                    __m128 XMMw2 = _mm_load_ps(w2);
+
+                    __m128 XMMwg1 = _mm_load_ps(wg1);
+                    __m128 XMMwg2 = _mm_load_ps(wg2);
 
                     __m128 XMMg1 = _mm_add_ps(
                                    _mm_mul_ps(XMMlambda, XMMw1),
@@ -99,19 +144,19 @@ inline ffm_float wTx(
                     XMMw2 = _mm_sub_ps(XMMw2, _mm_mul_ps(XMMeta, 
                             _mm_mul_ps(_mm_rsqrt_ps(XMMwg2), XMMg2)));
 
-                    _mm_store_ps(w1+d, XMMw1);
-                    _mm_store_ps(w2+d, XMMw2);
+                    _mm_store_ps(w1, XMMw1);
+                    _mm_store_ps(w2, XMMw2);
 
-                    _mm_store_ps(wg1+d, XMMwg1);
-                    _mm_store_ps(wg2+d, XMMwg2);
+                    _mm_store_ps(wg1, XMMwg1);
+                    _mm_store_ps(wg2, XMMwg2);
                 }
             }
             else
             {
-                for(ffm_int d = 0; d < model.k; d += 4)
+                for(ffm_int d = 0; d < align0; d += kALIGN * 2)
                 {
-                    __m128  XMMw1 = _mm_load_ps(w1+d);
-                    __m128  XMMw2 = _mm_load_ps(w2+d);
+                    __m128  XMMw1 = _mm_load_ps(w1_base+d);
+                    __m128  XMMw2 = _mm_load_ps(w2_base+d);
 
                     XMMt = _mm_add_ps(XMMt, 
                            _mm_mul_ps(_mm_mul_ps(XMMw1, XMMw2), XMMv));
@@ -131,583 +176,269 @@ inline ffm_float wTx(
     return t;
 }
 
-ffm_float* malloc_aligned_float(ffm_long size)
-{
-    void *ptr;
-
-#ifdef _WIN32
-    ptr = _aligned_malloc(size*sizeof(ffm_float), kALIGNByte);
-    if(ptr == nullptr)
-        throw bad_alloc();
 #else
-    int status = posix_memalign(&ptr, kALIGNByte, size*sizeof(ffm_float));
-    if(status != 0)
-        throw bad_alloc();
-#endif
-    
-    return (ffm_float*)ptr;
-}
-
-ffm_model* init_model(ffm_int n, ffm_int m, ffm_parameter param)
-{
-    ffm_int k_aligned = (ffm_int)ceil((ffm_double)param.k/kALIGN)*kALIGN;
-
-    ffm_model *model = new ffm_model;
-    model->n = n;
-    model->k = k_aligned;
-    model->m = m;
-    model->W = nullptr;
-    model->normalization = param.normalization;
-    
-    try
-    {
-        model->W = malloc_aligned_float((ffm_long)n*m*k_aligned*2);
-    }
-    catch(bad_alloc const &e)
-    {
-        ffm_destroy_model(&model);
-        throw;
-    }
 
-    ffm_float coef = 1.0f/sqrt(param.k);
-    ffm_float *w = model->W;
-
-    default_random_engine generator;
-    uniform_real_distribution<ffm_float> distribution(0.0, 1.0);
-
-    for(ffm_int j = 0; j < model->n; j++)
-    {
-        for(ffm_int f = 0; f < model->m; f++)
-        {
-            for(ffm_int d = 0; d < param.k; d++, w++)
-                *w = coef*distribution(generator);
-            for(ffm_int d = param.k; d < k_aligned; d++, w++)
-                *w = 0;
-            for(ffm_int d = k_aligned; d < 2*k_aligned; d++, w++)
-                *w = 1;
-        }
-    }
-
-    return model;
-}
-
-void shrink_model(ffm_model &model, ffm_int k_new)
-{
-    for(ffm_int j = 0; j < model.n; j++)
-    {
-        for(ffm_int f = 0; f < model.m; f++)
-        {
-            ffm_float *src = model.W + ((ffm_long)j*model.m+f)*model.k*2;
-            ffm_float *dst = model.W + ((ffm_long)j*model.m+f)*k_new;
-            copy(src, src+k_new, dst);
-        }
-    }
-
-    model.k = k_new;
-}
-
-vector<ffm_float> normalize(ffm_problem &prob)
-{
-    vector<ffm_float> R(prob.l);
-#if defined USEOMP
-#pragma omp parallel for schedule(static)
-#endif
-    for(ffm_int i = 0; i < prob.l; i++)
-    {
-        ffm_float norm = 0;
-        for(ffm_long p = prob.P[i]; p < prob.P[i+1]; p++)
-            norm += prob.X[p].v*prob.X[p].v;
-        R[i] = 1/norm;
-    }
+inline ffm_float wTx(
+    ffm_node *begin,
+    ffm_node *end,
+    ffm_float r,
+    ffm_model &model, 
+    ffm_float kappa=0, 
+    ffm_float eta=0, 
+    ffm_float lambda=0, 
+    bool do_update=false) {
 
-    return R;
-}
+    ffm_int align0 = 2 * get_k_aligned(model.k);
+    ffm_int align1 = model.m * align0;
 
-shared_ptr<ffm_model> train(
-    ffm_problem *tr, 
-    vector<ffm_int> &order, 
-    ffm_parameter param, 
-    ffm_problem *va=nullptr)
-{
-#if defined USEOMP
-    ffm_int old_nr_threads = omp_get_num_threads();
-    omp_set_num_threads(param.nr_threads);
-#endif
+    ffm_float t = 0;
+    for(ffm_node *N1 = begin; N1 != end; N1++) {
+        ffm_int j1 = N1->j;
+        ffm_int f1 = N1->f;
+        ffm_float v1 = N1->v;
+        if(j1 >= model.n || f1 >= model.m)
+            continue;
 
-    shared_ptr<ffm_model> model = 
-        shared_ptr<ffm_model>(init_model(tr->n, tr->m, param),
-            [] (ffm_model *ptr) { ffm_destroy_model(&ptr); });
+        for(ffm_node *N2 = N1+1; N2 != end; N2++) {
+            ffm_int j2 = N2->j;
+            ffm_int f2 = N2->f;
+            ffm_float v2 = N2->v;
+            if(j2 >= model.n || f2 >= model.m)
+                continue;
 
-    vector<ffm_float> R_tr, R_va;
-    if(param.normalization)
-    {
-        R_tr = normalize(*tr);
-        if(va != nullptr)
-            R_va = normalize(*va);
-    }
-    else
-    {
-        R_tr = vector<ffm_float>(tr->l, 1);
-        if(va != nullptr)
-            R_va = vector<ffm_float>(va->l, 1);
-    }
+            ffm_float *w1 = model.W + j1*align1 + f2*align0;
+            ffm_float *w2 = model.W + j2*align1 + f1*align0;
 
-    bool auto_stop = param.auto_stop && va != nullptr && va->l != 0;
+            ffm_float v = v1 * v2 * r;
 
-    ffm_int k_aligned = (ffm_int)ceil((ffm_double)param.k/kALIGN)*kALIGN;
-    ffm_long w_size = (ffm_long)model->n * model->m * k_aligned * 2;
-    vector<ffm_float> prev_W;
-    if(auto_stop)
-        prev_W.assign(w_size, 0);
-    ffm_double best_va_loss = numeric_limits<ffm_double>::max();
+            if(do_update) {
+                ffm_float *wg1 = w1 + kALIGN;
+                ffm_float *wg2 = w2 + kALIGN;
+                for(ffm_int d = 0; d < align0; d += kALIGN * 2)
+                {
+                    ffm_float g1 = lambda * w1[d] + kappa * w2[d] * v;
+                    ffm_float g2 = lambda * w2[d] + kappa * w1[d] * v;
 
-    if(!param.quiet)
-    {
-        if(param.auto_stop && (va == nullptr || va->l == 0))
-            cerr << "warning: ignoring auto-stop because there is no validation set" << endl;
+                    wg1[d] += g1 * g1;
+                    wg2[d] += g2 * g2;
 
-        cout.width(4);
-        cout << "iter";
-        cout.width(13);
-        cout << "tr_logloss";
-        if(va != nullptr && va->l != 0)
-        {
-            cout.width(13);
-            cout << "va_logloss";
+                    w1[d] -= eta / sqrt(wg1[d]) * g1;
+                    w2[d] -= eta / sqrt(wg2[d]) * g2;
+                }
+            } else {
+                for(ffm_int d = 0; d < align0; d += kALIGN * 2)
+                    t += w1[d] * w2[d] * v;
+            }
         }
-        cout << endl;
     }
 
-    for(ffm_int iter = 1; iter <= param.nr_iters; iter++)
-    {
-        ffm_double tr_loss = 0;
-        if(param.random)
-            random_shuffle(order.begin(), order.end());
-#if defined USEOMP
-#pragma omp parallel for schedule(static) reduction(+: tr_loss)
+    return t;
+}
 #endif
-        for(ffm_int ii = 0; ii < (ffm_int)order.size(); ii++)
-        {
-            ffm_int i = order[ii];
-
-            ffm_float y = tr->Y[i];
-            
-            ffm_node *begin = &tr->X[tr->P[i]];
-
-            ffm_node *end = &tr->X[tr->P[i+1]];
 
-            ffm_float r = R_tr[i];
+ffm_float* malloc_aligned_float(ffm_long size)
+{
+    void *ptr;
 
-            ffm_float t = wTx(begin, end, r, *model);
+#ifndef USESSE
 
-            ffm_float expnyt = exp(-y*t);
+    ptr = malloc(size * sizeof(ffm_float));
 
-            tr_loss += log(1+expnyt);
-               
-            ffm_float kappa = -y*expnyt/(1+expnyt);
+#else
 
-            wTx(begin, end, r, *model, kappa, param.eta, param.lambda, true);
-        }
+    #ifdef _WIN32
+        ptr = _aligned_malloc(size*sizeof(ffm_float), kALIGNByte);
+        if(ptr == nullptr)
+            throw bad_alloc();
+    #else
+        int status = posix_memalign(&ptr, kALIGNByte, size*sizeof(ffm_float));
+        if(status != 0)
+            throw bad_alloc();
+    #endif
 
-        if(!param.quiet)
-        {
-            tr_loss /= tr->l;
-
-            cout.width(4);
-            cout << iter;
-            cout.width(13);
-            cout << fixed << setprecision(5) << tr_loss;
-            if(va != nullptr && va->l != 0)
-            {
-                ffm_double va_loss = 0;
-#if defined USEOMP
-#pragma omp parallel for schedule(static) reduction(+:va_loss)
 #endif
-                for(ffm_int i = 0; i < va->l; i++)
-                {
-                    ffm_float y = va->Y[i];
-
-                    ffm_node *begin = &va->X[va->P[i]];
-
-                    ffm_node *end = &va->X[va->P[i+1]];
-
-                    ffm_float r = R_va[i];
+    
+    return (ffm_float*)ptr;
+}
 
-                    ffm_float t = wTx(begin, end, r, *model);
-                    
-                    ffm_float expnyt = exp(-y*t);
+ffm_model init_model(ffm_int n, ffm_int m, ffm_parameter param)
+{
+    ffm_model model;
+    model.n = n;
+    model.k = param.k;
+    model.m = m;
+    model.W = nullptr;
+    model.normalization = param.normalization;
+
+    ffm_int k_aligned = get_k_aligned(model.k);
+    
+    model.W = malloc_aligned_float((ffm_long)n*m*k_aligned*2);
 
-                    va_loss += log(1+expnyt);
-                }
-                va_loss /= va->l;
+    ffm_float coef = 1.0f / sqrt(model.k);
+    ffm_float *w = model.W;
 
-                cout.width(13);
-                cout << fixed << setprecision(5) << va_loss;
+    default_random_engine generator;
+    uniform_real_distribution<ffm_float> distribution(0.0, 1.0);
 
-                if(auto_stop)
-                {
-                    if(va_loss > best_va_loss)
-                    {
-                        memcpy(model->W, prev_W.data(), w_size*sizeof(ffm_float));
-                        cout << endl << "Auto-stop. Use model at " << iter-1 << "th iteration." << endl;
-                        break;
-                    }
-                    else
-                    {
-                        memcpy(prev_W.data(), model->W, w_size*sizeof(ffm_float));
-                        best_va_loss = va_loss; 
-                    }
+    for(ffm_int j = 0; j < model.n; j++) {
+        for(ffm_int f = 0; f < model.m; f++) {
+            for(ffm_int d = 0; d < k_aligned;) {
+                for(ffm_int s = 0; s < kALIGN; s++, w++, d++) {
+                    w[0] = (d < model.k)? coef * distribution(generator) : 0.0;
+                    w[kALIGN] = 1;
                 }
+                w += kALIGN;
             }
-            cout << endl;
         }
     }
 
-    shrink_model(*model, param.k);
-
-#if defined USEOMP
-    omp_set_num_threads(old_nr_threads);
-#endif
-
     return model;
 }
 
-// TODO: This function will be merged with train().
-shared_ptr<ffm_model> train_on_disk(
-    string tr_path,
-    string va_path,
-    ffm_parameter param)
-{
-#if defined USEOMP
-    ffm_int old_nr_threads = omp_get_num_threads();
-    omp_set_num_threads(param.nr_threads);
-#endif
-
-    FILE *f_tr = fopen(tr_path.c_str(), "rb");
-    FILE *f_va = nullptr;
-    if(!va_path.empty())
-        f_va = fopen(va_path.c_str(), "rb");
-
-    ffm_int m, n, max_l;
-    ffm_long max_nnz;
-    fread(&m, sizeof(ffm_int), 1, f_tr);
-    fread(&n, sizeof(ffm_int), 1, f_tr);
-    fread(&max_l, sizeof(ffm_int), 1, f_tr);
-    fread(&max_nnz, sizeof(ffm_long), 1, f_tr);
-
-    shared_ptr<ffm_model> model = 
-        shared_ptr<ffm_model>(init_model(n, m, param),
-            [] (ffm_model *ptr) { ffm_destroy_model(&ptr); });
-
+struct disk_problem_meta {
+    ffm_int n = 0;
+    ffm_int m = 0;
+    ffm_int l = 0;
+    ffm_int num_blocks = 0;
+    ffm_long B_pos = 0;
+    uint64_t hash1;
+    uint64_t hash2;
+};
+
+struct problem_on_disk {
+    disk_problem_meta meta;
     vector<ffm_float> Y;
-    Y.reserve(max_l);
     vector<ffm_float> R;
-    R.reserve(max_l);
     vector<ffm_long> P;
-    P.reserve(max_l+1);
     vector<ffm_node> X;
-    X.reserve(max_nnz);
-
-    bool auto_stop = param.auto_stop && !va_path.empty();
-
-    ffm_int k_aligned = (ffm_int)ceil((ffm_double)param.k/kALIGN)*kALIGN;
-    ffm_long w_size = (ffm_long)model->n * model->m * k_aligned * 2;
-    vector<ffm_float> prev_W;
-    if(auto_stop)
-        prev_W.assign(w_size, 0);
-    ffm_double best_va_loss = numeric_limits<ffm_double>::max();
-
-    if(!param.quiet)
-    {
-        if(param.auto_stop && va_path.empty())
-            cerr << "warning: ignoring auto-stop because there is no validation set" << endl;
-        cout.width(4);
-        cout << "iter";
-        cout.width(13);
-        cout << "tr_logloss";
-        if(!va_path.empty())
-        {
-            cout.width(13);
-            cout << "va_logloss";
+    vector<ffm_long> B;
+
+    problem_on_disk(string path) {
+        f.open(path, ios::in | ios::binary);
+        if(f.good()) {
+            f.read(reinterpret_cast<char*>(&meta), sizeof(disk_problem_meta));
+            f.seekg(meta.B_pos);
+            B.resize(meta.num_blocks);
+            f.read(reinterpret_cast<char*>(B.data()), sizeof(ffm_long) * meta.num_blocks);
         }
-        cout << endl;
     }
 
-    for(ffm_int iter = 1; iter <= param.nr_iters; iter++)
-    {
-        ffm_double tr_loss = 0;
-
-        fseek(f_tr, 3*sizeof(ffm_int)+sizeof(ffm_long), SEEK_SET);
-
-        ffm_int tr_l = 0;
-        while(true)
-        {
-            ffm_int l;
-            fread(&l, sizeof(ffm_int), 1, f_tr);
-            tr_l += l;
-            if(l == 0)
-                break;
-
-            Y.resize(l);
-            fread(Y.data(), sizeof(ffm_float), l, f_tr);
-
-            R.resize(l);
-            fread(R.data(), sizeof(ffm_float), l, f_tr);
-
-            P.resize(l+1);
-            fread(P.data(), sizeof(ffm_long), l+1, f_tr);
+    int load_block(int block_index) {
+        if(block_index >= meta.num_blocks)
+            assert(false);
 
-            X.resize(P[l]);
-            fread(X.data(), sizeof(ffm_node), P[l], f_tr);
+        f.seekg(B[block_index]);
 
-#if defined USEOMP
-#pragma omp parallel for schedule(static) reduction(+: tr_loss)
-#endif
-            for(ffm_int i = 0; i < l; i++)
-            {
-                ffm_float y = Y[i];
-                
-                ffm_node *begin = &X[P[i]];
-
-                ffm_node *end = &X[P[i+1]];
-
-                ffm_float r = param.normalization? R[i] : 1;
+        ffm_int l;
+        f.read(reinterpret_cast<char*>(&l), sizeof(ffm_int));
 
-                ffm_float t = wTx(begin, end, r, *model);
+        Y.resize(l);
+        f.read(reinterpret_cast<char*>(Y.data()), sizeof(ffm_float) * l);
 
-                ffm_float expnyt = exp(-y*t);
-
-                tr_loss += log(1+expnyt);
-                   
-                ffm_float kappa = -y*expnyt/(1+expnyt);
+        R.resize(l);
+        f.read(reinterpret_cast<char*>(R.data()), sizeof(ffm_float) * l);
 
-                wTx(begin, end, r, *model, kappa, param.eta, param.lambda, true);
-            }
-        }
+        P.resize(l+1);
+        f.read(reinterpret_cast<char*>(P.data()), sizeof(ffm_long) * (l+1));
 
-        if(!param.quiet)
-        {
-            tr_loss /= tr_l;
+        X.resize(P[l]);
+        f.read(reinterpret_cast<char*>(X.data()), sizeof(ffm_node) * P[l]);
 
-            cout.width(4);
-            cout << iter;
-            cout.width(13);
-            cout << fixed << setprecision(5) << tr_loss;
-
-            if(f_va != nullptr)
-            {
-                fseek(f_va, 3*sizeof(ffm_int)+sizeof(ffm_long), SEEK_SET);
-
-                ffm_int va_l = 0;
-                ffm_double va_loss = 0;
-                while(true)
-                {
-                    ffm_int l;
-                    fread(&l, sizeof(ffm_int), 1, f_va);
-                    va_l += l;
-                    if(l == 0)
-                        break;
-
-                    vector<ffm_float> Y(l);
-                    fread(Y.data(), sizeof(ffm_float), l, f_va);
-
-                    vector<ffm_float> R(l);
-                    fread(R.data(), sizeof(ffm_float), l, f_va);
-
-                    vector<ffm_long> P(l+1);
-                    fread(P.data(), sizeof(ffm_long), l+1, f_va);
-
-                    vector<ffm_node> X(P[l]);
-                    fread(X.data(), sizeof(ffm_node), P[l], f_va);
-
-#if defined USEOMP
-#pragma omp parallel for schedule(static) reduction(+: va_loss)
-#endif
-                    for(ffm_int i = 0; i < l; i++)
-                    {
-                        ffm_float y = Y[i];
-                        
-                        ffm_node *begin = &X[P[i]];
-
-                        ffm_node *end = &X[P[i+1]];
-
-                        ffm_float r = param.normalization? R[i] : 1;
-
-                        ffm_float t = wTx(begin, end, r, *model);
-
-                        ffm_float expnyt = exp(-y*t);
-
-                        va_loss += log(1+expnyt);
-                    }
-                }
-                va_loss /= va_l;
-
-                cout.width(13);
-                cout << fixed << setprecision(5) << va_loss;
-
-                if(auto_stop)
-                {
-                    if(va_loss > best_va_loss)
-                    {
-                        memcpy(model->W, prev_W.data(), w_size*sizeof(ffm_float));
-                        cout << endl << "Auto-stop. Use model at " << iter-1 << "th iteration." << endl;
-                        break;
-                    }
-                    else
-                    {
-                        memcpy(prev_W.data(), model->W, w_size*sizeof(ffm_float));
-                        best_va_loss = va_loss; 
-                    }
-                }
-            }
-            cout << endl;
-        }
+        return l;
     }
 
-    shrink_model(*model, param.k);
-
-    fclose(f_tr);
-    if(!va_path.empty())
-        fclose(f_va);
-
-#if defined USEOMP
-    omp_set_num_threads(old_nr_threads);
-#endif
-
-    return model;
-}
+    bool is_empty() {
+        return meta.l == 0;
+    }
 
-} // unnamed namespace
+private:
+    ifstream f;
+};
 
-ffm_problem* ffm_read_problem(char const *path)
+uint64_t hashfile(string txt_path, bool one_block=false)
 {
-    if(strlen(path) == 0)
-        return nullptr;
-
-    FILE *f = fopen(path, "r");
-    if(f == nullptr)
-        return nullptr;
-
-    ffm_problem *prob = new ffm_problem;
-    prob->l = 0;
-    prob->n = 0;
-    prob->m = 0;
-    prob->X = nullptr;
-    prob->P = nullptr;
-    prob->Y = nullptr;
-
-    char line[kMaxLineSize];
+    ifstream f(txt_path, ios::ate | ios::binary);
+    if(f.bad())
+        return 0;
 
-    ffm_long nnz = 0;
-    for(; fgets(line, kMaxLineSize, f) != nullptr; prob->l++)
-    {
-        strtok(line, " \t");
-        for(; ; nnz++)
-        {
-            char *ptr = strtok(nullptr," \t");
-            if(ptr == nullptr || *ptr == '\n')
-                break;
+    ffm_long end = (ffm_long) f.tellg();
+    f.seekg(0, ios::beg);
+    assert(f.tellg() == 0);
+
+    uint64_t magic = 90359;
+    for(ffm_long pos = 0; pos < end; ) {
+        ffm_long next_pos = min(pos + kCHUNK_SIZE, end);
+        ffm_long size = next_pos - pos;
+        vector<char> buffer(kCHUNK_SIZE);
+        f.read(buffer.data(), size);
+
+        ffm_int i = 0;
+        while(i < size - 8) {
+            uint64_t x = *reinterpret_cast<uint64_t*>(buffer.data() + i);
+            magic = ( (magic + x) * (magic + x + 1) >> 1) + x;
+            i += 8;
         }
-    }
-    rewind(f);
-
-    prob->X = new ffm_node[nnz];
-    prob->P = new ffm_long[prob->l+1];
-    prob->Y = new ffm_float[prob->l];
-
-    ffm_long p = 0;
-    prob->P[0] = 0;
-    for(ffm_int i = 0; fgets(line, kMaxLineSize, f) != nullptr; i++)
-    {
-        char *y_char = strtok(line, " \t");
-        ffm_float y = (atoi(y_char)>0)? 1.0f : -1.0f;
-        prob->Y[i] = y;
-
-        for(; ; p++)
-        {
-            char *field_char = strtok(nullptr,":");
-            char *idx_char = strtok(nullptr,":");
-            char *value_char = strtok(nullptr," \t");
-            if(field_char == nullptr || *field_char == '\n')
-                break;
-
-            ffm_int field = atoi(field_char);
-            ffm_int idx = atoi(idx_char);
-            ffm_float value = atof(value_char);
-
-            prob->m = max(prob->m, field+1);
-            prob->n = max(prob->n, idx+1);
-            
-            prob->X[p].f = field;
-            prob->X[p].j = idx;
-            prob->X[p].v = value;
+        for(; i < size; i++) {
+            char x = buffer[i];
+            magic = ( (magic + x) * (magic + x + 1) >> 1) + x;
         }
-        prob->P[i+1] = p;
-    }
 
-    fclose(f);
+        pos = next_pos;
+        if(one_block)
+            break;
+    }
 
-    return prob;
+    return magic;
 }
 
-int ffm_read_problem_to_disk(char const *txt_path, char const *bin_path)
-{
-    FILE *f_txt = fopen(txt_path, "r");
+void txt2bin(string txt_path, string bin_path) {
+    
+    FILE *f_txt = fopen(txt_path.c_str(), "r");
     if(f_txt == nullptr)
-        return 1;
+        throw;
 
-    FILE *f_bin = fopen(bin_path, "wb");
-    if(f_bin == nullptr)
-        return 1;
+    ofstream f_bin(bin_path, ios::out | ios::binary);
 
     vector<char> line(kMaxLineSize);
 
-    ffm_int m = 0;
-    ffm_int n = 0;
-    ffm_int max_l = 0;
-    ffm_long max_nnz = 0;
     ffm_long p = 0;
+    disk_problem_meta meta;
 
     vector<ffm_float> Y;
     vector<ffm_float> R;
     vector<ffm_long> P(1, 0);
     vector<ffm_node> X;
+    vector<ffm_long> B;
 
-    auto write_chunk = [&] ()
-    {
+    auto write_chunk = [&] () {
+        B.push_back(f_bin.tellp());
         ffm_int l = Y.size();
         ffm_long nnz = P[l];
+        meta.l += l;
 
-        max_l = max(max_l, l);
-        max_nnz = max(max_nnz, nnz);
-
-        fwrite(&l, sizeof(ffm_int), 1, f_bin);
-        fwrite(Y.data(), sizeof(ffm_float), l, f_bin);
-        fwrite(R.data(), sizeof(ffm_float), l, f_bin);
-        fwrite(P.data(), sizeof(ffm_long), l+1, f_bin);
-        fwrite(X.data(), sizeof(ffm_node), nnz, f_bin);
+        f_bin.write(reinterpret_cast<char*>(&l), sizeof(ffm_int));
+        f_bin.write(reinterpret_cast<char*>(Y.data()), sizeof(ffm_float) * l);
+        f_bin.write(reinterpret_cast<char*>(R.data()), sizeof(ffm_float) * l);
+        f_bin.write(reinterpret_cast<char*>(P.data()), sizeof(ffm_long) * (l+1));
+        f_bin.write(reinterpret_cast<char*>(X.data()), sizeof(ffm_node) * nnz);
 
         Y.clear();
         R.clear();
         P.assign(1, 0);
         X.clear();
         p = 0;
+        meta.num_blocks++;
     };
 
-    fwrite(&m, sizeof(ffm_int), 1, f_bin);
-    fwrite(&n, sizeof(ffm_int), 1, f_bin);
-    fwrite(&max_l, sizeof(ffm_int), 1, f_bin);
-    fwrite(&max_nnz, sizeof(ffm_long), 1, f_bin);
+    f_bin.write(reinterpret_cast<char*>(&meta), sizeof(disk_problem_meta));
 
-    while(fgets(line.data(), kMaxLineSize, f_txt))
-    {
+    while(fgets(line.data(), kMaxLineSize, f_txt)) {
         char *y_char = strtok(line.data(), " \t");
 
         ffm_float y = (atoi(y_char)>0)? 1.0f : -1.0f;
 
         ffm_float scale = 0;
-        for(; ; p++)
-        {
+        for(; ; p++) {
             char *field_char = strtok(nullptr,":");
             char *idx_char = strtok(nullptr,":");
             char *value_char = strtok(nullptr," \t");
@@ -721,12 +452,12 @@ int ffm_read_problem_to_disk(char const *txt_path, char const *bin_path)
 
             X.push_back(N);
 
-            m = max(m, N.f+1);
-            n = max(n, N.j+1);
+            meta.m = max(meta.m, N.f+1);
+            meta.n = max(meta.n, N.j+1);
 
             scale += N.v*N.v;
         }
-        scale = 1/scale;
+        scale = 1.0 / scale;
 
         Y.push_back(y);
         R.push_back(scale);
@@ -736,310 +467,229 @@ int ffm_read_problem_to_disk(char const *txt_path, char const *bin_path)
             write_chunk(); 
     }
     write_chunk(); 
-    write_chunk(); 
-
-    rewind(f_bin);
-    fwrite(&m, sizeof(ffm_int), 1, f_bin);
-    fwrite(&n, sizeof(ffm_int), 1, f_bin);
-    fwrite(&max_l, sizeof(ffm_int), 1, f_bin);
-    fwrite(&max_nnz, sizeof(ffm_long), 1, f_bin);
+    write_chunk(); // write a dummy empty chunk in order to know where the EOF is
+    assert(meta.num_blocks == (ffm_int)B.size());
+    meta.B_pos = f_bin.tellp();
+    f_bin.write(reinterpret_cast<char*>(B.data()), sizeof(ffm_long) * B.size());
 
-    fclose(f_bin);
     fclose(f_txt);
+    meta.hash1 = hashfile(txt_path, true);
+    meta.hash2 = hashfile(txt_path, false);
 
-    return 0;
+    f_bin.seekp(0, ios::beg);
+    f_bin.write(reinterpret_cast<char*>(&meta), sizeof(disk_problem_meta));
 }
 
-void ffm_destroy_problem(ffm_problem **prob)
-{
-    if(prob == nullptr || *prob == nullptr)
-        return;
-    delete[] (*prob)->X;
-    delete[] (*prob)->P;
-    delete[] (*prob)->Y;
-    delete *prob;
-    *prob = nullptr;
-}
-
-ffm_int ffm_save_model(ffm_model *model, char const *path)
-{
-    ofstream f_out(path);
-    if(!f_out.is_open())
-        return 1;
-
-    f_out << "n " << model->n << "\n";
-    f_out << "m " << model->m << "\n";
-    f_out << "k " << model->k << "\n";
-    f_out << "normalization " << model->normalization << "\n";
-
-    ffm_float *ptr = model->W;
-    for(ffm_int j = 0; j < model->n; j++)
-    {
-        for(ffm_int f = 0; f < model->m; f++)
-        {
-            f_out << "w" << j << "," << f << " ";
-            for(ffm_int d = 0; d < model->k; d++, ptr++)
-                f_out << *ptr << " ";
-            f_out << "\n";
-        }
-    }
-
-    return 0;
+bool check_same_txt_bin(string txt_path, string bin_path) {
+    ifstream f_bin(bin_path, ios::binary | ios::ate);
+    if(f_bin.tellg() < (ffm_long)sizeof(disk_problem_meta))
+        return false;
+    disk_problem_meta meta;
+    f_bin.seekg(0, ios::beg);
+    f_bin.read(reinterpret_cast<char*>(&meta), sizeof(disk_problem_meta));
+    if(meta.hash1 != hashfile(txt_path, true))
+        return false;
+    if(meta.hash2 != hashfile(txt_path, false))
+        return false;
+
+    return true;
 }
 
-ffm_model* ffm_load_model(char const *path)
-{
-    ifstream f_in(path);
-    if(!f_in.is_open())
-        return nullptr;
-
-    string dummy;
-
-    ffm_model *model = new ffm_model;
-    model->W = nullptr;
-
-    f_in >> dummy >> model->n >> dummy >> model->m >> dummy >> model->k 
-         >> dummy >> model->normalization;
-
-    try
-    {
-        model->W = malloc_aligned_float((ffm_long)model->m*model->n*model->k);
-    }
-    catch(bad_alloc const &e)
-    {
-        ffm_destroy_model(&model);
-        return nullptr;
-    }
-
-    ffm_float *ptr = model->W;
-    for(ffm_int j = 0; j < model->n; j++)
-    {
-        for(ffm_int f = 0; f < model->m; f++)
-        {
-            f_in >> dummy;
-            for(ffm_int d = 0; d < model->k; d++, ptr++)
-                f_in >> *ptr;
-        }
-    }
-
-    return model;
-}
+} // unnamed namespace
 
-void ffm_destroy_model(ffm_model **model)
-{
-    if(model == nullptr || *model == nullptr)
-        return;
+ffm_model::~ffm_model() {
+    if(W != nullptr) {
 #ifdef _WIN32
-    _aligned_free((*model)->W);
+        _aligned_free(W);
 #else
-    free((*model)->W);
+        free(W);
 #endif
-    delete *model;
-    *model = nullptr;
-}
-
-ffm_parameter ffm_get_default_param()
-{
-    ffm_parameter param;
-
-    param.eta = 0.2;
-    param.lambda = 0.00002;
-    param.nr_iters = 15;
-    param.k = 4;
-    param.nr_threads = 1;
-    param.quiet = false;
-    param.normalization = true;
-    param.random = true;
-    param.auto_stop = false;
-
-    return param;
+        W = nullptr;
+    }
 }
 
-ffm_model* ffm_train_with_validation(ffm_problem *tr, ffm_problem *va, ffm_parameter param)
-{
-    vector<ffm_int> order(tr->l);
-    for(ffm_int i = 0; i < tr->l; i++)
-        order[i] = i;
-
-    shared_ptr<ffm_model> model = train(tr, order, param, va);
-
-    ffm_model *model_ret = new ffm_model;
-
-    model_ret->n = model->n;
-    model_ret->m = model->m;
-    model_ret->k = model->k;
-    model_ret->normalization = model->normalization;
-
-    model_ret->W = model->W;
-    model->W = nullptr;
-
-    return model_ret;
-}
+void ffm_read_problem_to_disk(string txt_path, string bin_path) {
 
-ffm_model* ffm_train(ffm_problem *prob, ffm_parameter param)
-{
-    return ffm_train_with_validation(prob, nullptr, param);
+    Timer timer;
+    
+    cout << "First check if the text file has already converted to binary format " << flush;
+    bool same_file = check_same_txt_bin(txt_path, bin_path);
+    cout << "(" << fixed << setprecision(1) << timer.toc() << " seconds)" << endl;
+
+    if(same_file) {
+        cout << "Binary file found. Skip converting text to binary" << endl;
+    } else {
+        cout << "Binary file NOT found. Convert text file to binary file " << flush;
+        txt2bin(txt_path, bin_path);
+        cout << "(" << fixed << setprecision(1) << timer.toc() << " seconds)" << endl;
+    }
 }
 
-ffm_model* ffm_train_with_validation_on_disk(
-    char const *tr_path,
-    char const *va_path,
-    ffm_parameter param)
-{
-    shared_ptr<ffm_model> model = train_on_disk(tr_path, va_path, param);
-
-    ffm_model *model_ret = new ffm_model;
+ffm_model ffm_train_on_disk(string tr_path, string va_path, ffm_parameter param) {
 
-    model_ret->n = model->n;
-    model_ret->m = model->m;
-    model_ret->k = model->k;
-    model_ret->normalization = model->normalization;
+    problem_on_disk tr(tr_path);
+    problem_on_disk va(va_path);
 
-    model_ret->W = model->W;
-    model->W = nullptr;
+    ffm_model model = init_model(tr.meta.n, tr.meta.m, param);
 
-    return model_ret;
-}
+    bool auto_stop = param.auto_stop && !va_path.empty();
 
-ffm_model* ffm_train_on_disk(char const *prob_path, ffm_parameter param)
-{
-    return ffm_train_with_validation_on_disk(prob_path, "", param);
-}
+    ffm_long w_size = get_w_size(model);
+    vector<ffm_float> prev_W(w_size, 0);
+    if(auto_stop)
+        prev_W.assign(w_size, 0);
+    ffm_double best_va_loss = numeric_limits<ffm_double>::max();
 
-ffm_float ffm_predict(ffm_node *begin, ffm_node *end, ffm_model *model)
-{
-    ffm_float r = 1;
-    if(model->normalization)
+    cout.width(4);
+    cout << "iter";
+    cout.width(13);
+    cout << "tr_logloss";
+    if(!va_path.empty())
     {
-        r = 0;
-        for(ffm_node *N = begin; N != end; N++)
-            r += N->v*N->v; 
-        r = 1/r;
+        cout.width(13);
+        cout << "va_logloss";
     }
+    cout.width(13);
+    cout << "tr_time";
+    cout << endl;
 
-    ffm_long align0 = (ffm_long)model->k;
-    ffm_long align1 = (ffm_long)model->m*align0;
+    Timer timer;
 
-    ffm_float t = 0;
-    for(ffm_node *N1 = begin; N1 != end; N1++)
-    {
-        ffm_int j1 = N1->j;
-        ffm_int f1 = N1->f;
-        ffm_float v1 = N1->v;
-        if(j1 >= model->n || f1 >= model->m)
-            continue;
+    auto one_epoch = [&] (problem_on_disk &prob, bool do_update) {
 
-        for(ffm_node *N2 = N1+1; N2 != end; N2++)
-        {
-            ffm_int j2 = N2->j;
-            ffm_int f2 = N2->f;
-            ffm_float v2 = N2->v;
-            if(j2 >= model->n || f2 >= model->m)
-                continue;
-
-            ffm_float *w1 = model->W + j1*align1 + f2*align0;
-            ffm_float *w2 = model->W + j2*align1 + f1*align0;
+        ffm_double loss = 0;
 
-            ffm_float v = v1*v2*r;
+        vector<ffm_int> outer_order(prob.meta.num_blocks);
+        iota(outer_order.begin(), outer_order.end(), 0);
+        random_shuffle(outer_order.begin(), outer_order.end());
+        for(auto blk : outer_order) {
+            ffm_int l = prob.load_block(blk);
 
-            for(ffm_int d = 0; d < model->k; d++)
-                t += w1[d]*w2[d]*v;
-        }
-    }
-
-    return 1/(1+exp(-t));
-}
+            vector<ffm_int> inner_order(l);
+            iota(inner_order.begin(), inner_order.end(), 0);
+            random_shuffle(inner_order.begin(), inner_order.end());
 
-ffm_float ffm_cross_validation(
-    ffm_problem *prob, 
-    ffm_int nr_folds,
-    ffm_parameter param)
-{
 #if defined USEOMP
-    ffm_int old_nr_threads = omp_get_num_threads();
-    omp_set_num_threads(param.nr_threads);
+#pragma omp parallel for schedule(static) reduction(+: loss)
 #endif
+            for(ffm_int ii = 0; ii < l; ii++) {
+                ffm_int i = inner_order[ii];
 
-    bool quiet = param.quiet;
-    param.quiet = true;
+                ffm_float y = prob.Y[i];
+                
+                ffm_node *begin = &prob.X[prob.P[i]];
 
-    vector<ffm_int> order(prob->l);
-    for(ffm_int i = 0; i < prob->l; i++)
-        order[i] = i;
-    random_shuffle(order.begin(), order.end());
+                ffm_node *end = &prob.X[prob.P[i+1]];
 
-    if(!quiet)
-    {
-        cout.width(4);
-        cout << "fold";
-        cout.width(13);
-        cout << "logloss";
-        cout << endl;
-    }
+                ffm_float r = param.normalization? prob.R[i] : 1;
 
-    ffm_double loss = 0;
-    ffm_int nr_instance_per_fold = prob->l/nr_folds;
-    for(ffm_int fold = 0; fold < nr_folds; fold++)
-    {
-        ffm_int begin = fold*nr_instance_per_fold;
-        ffm_int end = min(begin + nr_instance_per_fold, prob->l);
+                ffm_float t = wTx(begin, end, r, model);
 
-        vector<ffm_int> order1;
-        for(ffm_int i = 0; i < begin; i++)
-            order1.push_back(order[i]);
-        for(ffm_int i = end; i < prob->l; i++)
-            order1.push_back(order[i]);
+                ffm_float expnyt = exp(-y*t);
 
-        shared_ptr<ffm_model> model = train(prob, order1, param);
+                loss += log(1+expnyt);
 
-        ffm_double loss1 = 0;
-#if defined USEOMP
-#pragma omp parallel for schedule(static) reduction(+: loss1)
-#endif
-        for(ffm_int ii = begin; ii < end; ii++)
-        {
-            ffm_int i = order[ii];
+                if(do_update) {
+                   
+                    ffm_float kappa = -y*expnyt/(1+expnyt);
 
-            ffm_float y = prob->Y[i];
-            
-            ffm_node *begin = &prob->X[prob->P[i]];
+                    wTx(begin, end, r, model, kappa, param.eta, param.lambda, true);
+                }
+            }
+        }
 
-            ffm_node *end = &prob->X[prob->P[i+1]];
+        return loss / prob.meta.l;
+    };
 
-            ffm_float y_bar = ffm_predict(begin, end, model.get());
+    for(ffm_int iter = 1; iter <= param.nr_iters; iter++) {
+        timer.tic();
+        ffm_double tr_loss = one_epoch(tr, true);
+        timer.toc();
 
-            loss1 -= y==1? log(y_bar) : log(1-y_bar);
-        }
-        loss += loss1;
+        cout.width(4);
+        cout << iter;
+        cout.width(13);
+        cout << fixed << setprecision(5) << tr_loss;
+
+        if(!va.is_empty()) {
+            ffm_double va_loss = one_epoch(va, false);
 
-        if(!quiet)
-        {
-            cout.width(4);
-            cout << fold;
             cout.width(13);
-            cout << fixed << setprecision(4) << loss1 / (end-begin);
-            cout << endl;
+            cout << fixed << setprecision(5) << va_loss;
+
+            if(auto_stop) {
+                if(va_loss > best_va_loss) {
+                    memcpy(model.W, prev_W.data(), w_size*sizeof(ffm_float));
+                    cout << endl << "Auto-stop. Use model at " << iter-1 << "th iteration." << endl;
+                    break;
+                } else {
+                    memcpy(prev_W.data(), model.W, w_size*sizeof(ffm_float));
+                    best_va_loss = va_loss; 
+                }
+            }
         }
+        cout.width(13);
+        cout << fixed << setprecision(1) << timer.get() << endl;
     }
 
-    if(!quiet)
-    {
-        cout.width(17);
-        cout.fill('=');
-        cout << "" << endl;
-        cout.fill(' ');
-        cout.width(4);
-        cout << "avg";
-        cout.width(13);
-        cout << fixed << setprecision(4) << loss/prob->l;
-        cout << endl;
+    return model;
+}
+
+void ffm_save_model(ffm_model &model, string path) {
+    ofstream f_out(path, ios::out | ios::binary);
+    f_out.write(reinterpret_cast<char*>(&model.n), sizeof(ffm_int));
+    f_out.write(reinterpret_cast<char*>(&model.m), sizeof(ffm_int));
+    f_out.write(reinterpret_cast<char*>(&model.k), sizeof(ffm_int));
+    f_out.write(reinterpret_cast<char*>(&model.normalization), sizeof(bool));
+
+    ffm_long w_size = get_w_size(model);
+    // f_out.write(reinterpret_cast<char*>(model.W), sizeof(ffm_float) * w_size);
+    // Need to write chunk by chunk because some compiler use int32 and will overflow when w_size * 4 > MAX_INT
+
+    for(ffm_long offset = 0; offset < w_size; ) {
+        ffm_long next_offset = min(w_size, offset + (ffm_long) sizeof(ffm_float) * kCHUNK_SIZE);
+        ffm_long size = next_offset - offset;
+        f_out.write(reinterpret_cast<char*>(model.W+offset), sizeof(ffm_float) * size);
+        offset = next_offset;
     }
+}
 
-#if defined USEOMP
-    omp_set_num_threads(old_nr_threads);
-#endif
+ffm_model ffm_load_model(string path) {
+    ifstream f_in(path, ios::in | ios::binary);
+
+    ffm_model model;
+    f_in.read(reinterpret_cast<char*>(&model.n), sizeof(ffm_int));
+    f_in.read(reinterpret_cast<char*>(&model.m), sizeof(ffm_int));
+    f_in.read(reinterpret_cast<char*>(&model.k), sizeof(ffm_int));
+    f_in.read(reinterpret_cast<char*>(&model.normalization), sizeof(bool));
+
+    ffm_long w_size = get_w_size(model);
+    model.W = malloc_aligned_float(w_size);
+    // f_in.read(reinterpret_cast<char*>(model.W), sizeof(ffm_float) * w_size);
+    // Need to write chunk by chunk because some compiler use int32 and will overflow when w_size * 4 > MAX_INT
+
+    for(ffm_long offset = 0; offset < w_size; ) {
+        ffm_long next_offset = min(w_size, offset + (ffm_long) sizeof(ffm_float) * kCHUNK_SIZE);
+        ffm_long size = next_offset - offset;
+        f_in.read(reinterpret_cast<char*>(model.W+offset), sizeof(ffm_float) * size);
+        offset = next_offset;
+    }
 
-    return loss/prob->l;
+    return model;
+}
+
+ffm_float ffm_predict(ffm_node *begin, ffm_node *end, ffm_model &model) {
+    ffm_float r = 1;
+    if(model.normalization) {
+        r = 0;
+        for(ffm_node *N = begin; N != end; N++)
+            r += N->v*N->v; 
+        r = 1/r;
+    }
+
+    ffm_float t = wTx(begin, end, r, model);
+
+    return 1/(1+exp(-t));
 }
 
 } // namespace ffm
diff --git a/ffm.h b/ffm.h
index ab242a9..fb7b8d7 100644
--- a/ffm.h
+++ b/ffm.h
@@ -1,88 +1,51 @@
 #ifndef _LIBFFM_H
 #define _LIBFFM_H
 
-#ifdef __cplusplus
-extern "C" 
-{
+#include <string>
 
-namespace ffm
-{
-#endif
+namespace ffm {
+
+using namespace std;
 
 typedef float ffm_float;
 typedef double ffm_double;
 typedef int ffm_int;
 typedef long long ffm_long;
 
-struct ffm_node
-{
-    ffm_int f;
-    ffm_int j;
-    ffm_float v;
-};
-
-struct ffm_problem
-{
-    ffm_int n;
-    ffm_int l;
-    ffm_int m;
-    ffm_node *X;
-    ffm_long *P;
-    ffm_float *Y;
+struct ffm_node {
+    ffm_int f; // field index
+    ffm_int j; // feature index
+    ffm_float v; // value
 };
 
-struct ffm_model
-{
-    ffm_int n;
-    ffm_int m;
-    ffm_int k;
-    ffm_float *W;
+struct ffm_model {
+    ffm_int n; // number of features
+    ffm_int m; // number of fields
+    ffm_int k; // number of latent factors
+    ffm_float *W = nullptr;
     bool normalization;
+    ~ffm_model();
 };
 
-struct ffm_parameter
-{
-    ffm_float eta;
-    ffm_float lambda;
-    ffm_int nr_iters;
-    ffm_int k;
-    ffm_int nr_threads;
-    bool quiet;
-    bool normalization;
-    bool random;
-    bool auto_stop;
+struct ffm_parameter {
+    ffm_float eta = 0.2; // learning rate
+    ffm_float lambda = 0.00002; // regularization parameter
+    ffm_int nr_iters = 15;
+    ffm_int k = 4; // number of latent factors
+    bool normalization = true;
+    bool auto_stop = false;
 };
 
-ffm_problem* ffm_read_problem(char const *path);
-
-int ffm_read_problem_to_disk(char const *txt_path, char const *bin_path);
-
-void ffm_destroy_problem(struct ffm_problem **prob);
-
-ffm_int ffm_save_model(ffm_model *model, char const *path);
+void ffm_read_problem_to_disk(string txt_path, string bin_path);
 
-ffm_model* ffm_load_model(char const *path);
+void ffm_save_model(ffm_model &model, string path);
 
-void ffm_destroy_model(struct ffm_model **model);
+ffm_model ffm_load_model(string path);
 
-ffm_parameter ffm_get_default_param();
+ffm_model ffm_train_on_disk(string Tr_path, string Va_path, ffm_parameter param);
 
-ffm_model* ffm_train_with_validation(struct ffm_problem *Tr, struct ffm_problem *Va, struct ffm_parameter param);
+ffm_float ffm_predict(ffm_node *begin, ffm_node *end, ffm_model &model);
 
-ffm_model* ffm_train(struct ffm_problem *prob, struct ffm_parameter param);
-
-ffm_model* ffm_train_with_validation_on_disk(char const *Tr_path, char const *Va_path, struct ffm_parameter param);
-
-ffm_model* ffm_train_on_disk(char const *path, struct ffm_parameter param);
-
-ffm_float ffm_predict(ffm_node *begin, ffm_node *end, ffm_model *model);
-
-ffm_float ffm_cross_validation(struct ffm_problem *prob, ffm_int nr_folds, struct ffm_parameter param);
-
-#ifdef __cplusplus
 } // namespace ffm
 
-} // extern "C"
-#endif
-
 #endif // _LIBFFM_H
diff --git a/timer.cpp b/timer.cpp
new file mode 100644
index 0000000..00f2be6
--- /dev/null
+++ b/timer.cpp
@@ -0,0 +1,31 @@
+#include <string>
+#include "timer.h"
+
+Timer::Timer()
+{
+    reset();
+}
+
+void Timer::reset()
+{
+    begin = std::chrono::high_resolution_clock::now();
+    duration = 
+        std::chrono::duration_cast<std::chrono::milliseconds>(begin-begin);
+}
+
+void Timer::tic()
+{
+    begin = std::chrono::high_resolution_clock::now();
+}
+
+float Timer::toc()
+{
+    duration += std::chrono::duration_cast<std::chrono::milliseconds>
+                    (std::chrono::high_resolution_clock::now()-begin);
+    return get();
+}
+
+float Timer::get()
+{
+    return (float)duration.count() / 1000;
+}
diff --git a/timer.h b/timer.h
new file mode 100644
index 0000000..444bb50
--- /dev/null
+++ b/timer.h
@@ -0,0 +1,14 @@
+#include <chrono>
+
+class Timer
+{
+public:
+    Timer();
+    void reset();
+    void tic();
+    float toc();
+    float get();
+private:
+    std::chrono::high_resolution_clock::time_point begin;
+    std::chrono::milliseconds duration;
+};