Merge pull request #1348 from ginkgo-project/fix_typos

Fix typos

.github/_typos.toml

@@ -0,0 +1,23 @@
+[files]
+extend-exclude = ["third_party/*", "*.svg"]
+
+[default.extend-words]
+dout = "dout"
+nd = "nd"
+tht = "tht"
+automatical = "automatical"
+strat = "strat"
+entrie = "entrie"
+agregate = "agregate" # since that script name is already in ginkgo-data repo
+
+[default.extend-identifiers]
+set_complex_subpsace = "set_complex_subpsace" # remove when deprecated function is gone
+HSA_HEADER = "HSA_HEADER"
+conj_operaton = "conj_operaton" # considered interface break in range.hpp
+imag_operaton = "imag_operaton" # considered interface break in range.hpp
+real_operaton = "real_operaton" # considered interface break in range.hpp
+one_operaton = "one_operaton" # considered interface break in range.hpp
+abs_operaton = "abs_operaton" # considered interface break in range.hpp
+max_operaton = "max_operaton" # considered interface break in range.hpp
+min_operaton = "min_operaton" # considered interface break in range.hpp
+squared_norm_operaton = "squared_norm_operaton" # considered interface break in range.hpp

.github/workflows/spell_check.yml

@@ -0,0 +1,16 @@
+name: Test GitHub Action
+on:
+  pull_request:
+    types: [opened, synchronize]
+
+jobs:
+  run:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - name: Check for typos
+      uses: crate-ci/typos@master
+      with:
+        config: .github/_typos.toml
+

CHANGELOG.md

@@ -215,7 +215,7 @@ Supported systems and requirements:
 + Add reduce_add for arrays ([#831](https://github.com/ginkgo-project/ginkgo/pull/831))
 + Add utility to simplify Dense View creation from an existing Dense vector ([#1136](https://github.com/ginkgo-project/ginkgo/pull/1136)).
 + Add a custom transpose implementation for Fbcsr and Csr transpose for unsupported vendor types ([#1123](https://github.com/ginkgo-project/ginkgo/pull/1123))
-+ Make IDR random initilization deterministic ([#1116](https://github.com/ginkgo-project/ginkgo/pull/1116))
++ Make IDR random initialization deterministic ([#1116](https://github.com/ginkgo-project/ginkgo/pull/1116))
 + Move the algorithm choice for triangular solvers from Csr::strategy_type to a factory parameter ([#1088](https://github.com/ginkgo-project/ginkgo/pull/1088))
 + Update CUDA archCoresPerSM ([#1175](https://github.com/ginkgo-project/ginkgo/pull/1116))
 + Add kernels for Csr sparsity pattern lookup ([#994](https://github.com/ginkgo-project/ginkgo/pull/994))
@@ -620,7 +620,7 @@ page](https://github.com/ginkgo-project/ginkgo/wiki/Known-Issues).
 
 
 ### Additions
-+ Upper and lower triangular solvers ([#327](https://github.com/ginkgo-project/ginkgo/issues/327), [#336](https://github.com/ginkgo-project/ginkgo/issues/336), [#341](https://github.com/ginkgo-project/ginkgo/issues/341), [#342](https://github.com/ginkgo-project/ginkgo/issues/342)) 
++ Upper and lower triangular solvers ([#327](https://github.com/ginkgo-project/ginkgo/issues/327), [#336](https://github.com/ginkgo-project/ginkgo/issues/336), [#341](https://github.com/ginkgo-project/ginkgo/issues/341), [#342](https://github.com/ginkgo-project/ginkgo/issues/342))
 + New factorization support in Ginkgo, and addition of the ParILU
   algorithm ([#305](https://github.com/ginkgo-project/ginkgo/issues/305), [#315](https://github.com/ginkgo-project/ginkgo/issues/315), [#319](https://github.com/ginkgo-project/ginkgo/issues/319), [#324](https://github.com/ginkgo-project/ginkgo/issues/324))
 + New ILU preconditioner ([#348](https://github.com/ginkgo-project/ginkgo/issues/348), [#353](https://github.com/ginkgo-project/ginkgo/issues/353))
@@ -632,7 +632,7 @@ page](https://github.com/ginkgo-project/ginkgo/wiki/Known-Issues).
 + Allow benchmarking CuSPARSE spmv formats through Ginkgo's benchmarks ([#303](https://github.com/ginkgo-project/ginkgo/issues/303))
 + New benchmark for sparse matrix format conversions ([#312](https://github.com/ginkgo-project/ginkgo/issues/312)[#317](https://github.com/ginkgo-project/ginkgo/issues/317))
 + Add conversions between CSR and Hybrid formats ([#302](https://github.com/ginkgo-project/ginkgo/issues/302), [#310](https://github.com/ginkgo-project/ginkgo/issues/310))
-+ Support for sorting rows in the CSR format by column idices ([#322](https://github.com/ginkgo-project/ginkgo/issues/322))
++ Support for sorting rows in the CSR format by column indices ([#322](https://github.com/ginkgo-project/ginkgo/issues/322))
 + Addition of a CUDA COO SpMM kernel for improved performance ([#345](https://github.com/ginkgo-project/ginkgo/issues/345))
 + Addition of a LinOp to handle perturbations of the form (identity + scalar *
   basis * projector) ([#334](https://github.com/ginkgo-project/ginkgo/issues/334))
@@ -847,7 +847,7 @@ Ginkgo 1.0.0 is brought to you by:
 
 **Karlsruhe Institute of Technology**, Germany  
 **Universitat Jaume I**, Spain  
-**University of Tennessee, Knoxville**, US   
+**University of Tennessee, Knoxville**, US  
 
 These universities, along with various project grants, supported the development team and provided resources needed for the development of Ginkgo.
 
@@ -859,7 +859,7 @@ Ginkgo 1.0.0 contains contributions from:
 **Goran Flegar**, Universitat Jaume I  
 **Fritz Göbel**, Karlsruhe Institute of Technology  
 **Thomas Grützmacher**, Karlsruhe Institute of Technology  
-**Pratik Nayak**, Karlsruhe Institue of Technologgy  
+**Pratik Nayak**, Karlsruhe Institute of Technology  
 **Tobias Ribizel**, Karlsruhe Institute of Technology  
 **Yuhsiang Tsai**, National Taiwan University  
 
@@ -869,11 +869,11 @@ Supporting materials are provided by the following individuals:
 **Frithjof Fleischhammer** - the Ginkgo website  
 
 The development team is grateful to the following individuals for discussions and comments:
- 
+
 **Erik Boman**  
 **Jelena Držaić**  
 **Mike Heroux**  
 **Mark Hoemmen**  
-**Timo Heister**    
+**Timo Heister**  
 **Jens Saak**  
 

CMakeLists.txt

@@ -68,7 +68,7 @@ endif()
 set(GINKGO_CUDA_COMPILER_FLAGS "" CACHE STRING
     "Set the required NVCC compiler flags, mainly used for warnings. Current default is an empty string")
 set(GINKGO_CUDA_ARCHITECTURES "Auto" CACHE STRING
-    "A list of target NVIDIA GPU achitectures. See README.md for more detail.")
+    "A list of target NVIDIA GPU architectures. See README.md for more detail.")
 option(GINKGO_CUDA_DEFAULT_HOST_COMPILER "Tell Ginkgo to not automatically set the CUDA host compiler" OFF)
 # the details of fine/coarse grain memory and unsafe atomic are available https://docs.olcf.ornl.gov/systems/crusher_quick_start_guide.html#floating-point-fp-atomic-operations-and-coarse-fine-grained-memory-allocations
 option(GINKGO_HIP_AMD_UNSAFE_ATOMIC "Compiler uses unsafe floating point atomic (only for AMD GPU and ROCM >= 5). Default is ON because we use hipMalloc, which is always on coarse grain. Must turn off when allocating memory on fine grain" ON)

CONTRIBUTING.md

@@ -312,7 +312,7 @@ Thus, contributors should be aware of the following rules for blank lines:
         However, simply calling function `f` from function `g` does not imply
         that `f` and `g` are "related".
 2.  Statements within structures / classes are separated with 1 blank line.
-    There are no blank lines betweeen the first / last statement in the
+    There are no blank lines between the first / last statement in the
     structure / class.
     1.  _exception_: there is no blank line between an access modifier (`private`, `protected`, `public`) and the following statement.
        _example_:

accessor/accessor_helper.hpp

@@ -78,7 +78,7 @@ struct row_major_helper_s {
         const std::array<SizeType, (total_dim > 1 ? total_dim - 1 : 0)>& stride,
         IndexType first, Indices&&... idxs)
     {
-        // The ASSERT size check must NOT be indexed with `dim_idx` directy,
+        // The ASSERT size check must NOT be indexed with `dim_idx` directly,
         // otherwise, it leads to a linker error. The reason is likely that
         // `std::array<size_type, N>::operator[](const size_type &)` uses a
         // reference. Since `dim_idx` is constexpr (and not defined in a

accessor/row_major.hpp

@@ -55,7 +55,7 @@ namespace acc {
  * constructor parameters for this class to the range (it will forward it to
  * this class).
  *
- * @warning For backward compatability reasons, a specialization is provided
+ * @warning For backward compatibility reasons, a specialization is provided
  *          for dimensionality == 2.
  *
  * @tparam ValueType  type of values this accessor returns

accessor/utils.hpp

@@ -243,7 +243,7 @@ to_arithmetic_type(const Ref& ref)
  * @internal
  * Struct used for testing if an implicit cast is present. The constructor only
  * takes an OutType, so any argument of a type that is not implicitly
- * convertable to OutType is incompatible.
+ * convertible to OutType is incompatible.
  */
 template <typename OutType>
 struct test_for_implicit_cast {

benchmark/tools/mtx_to_binary.cpp

@@ -61,8 +61,8 @@ void process(const char* input, const char* output, bool validate)
         }
     }
     if (validate) {
-        std::ifstream ois(output, std::ios_base::in | std::ios_base::binary);
-        auto data2 = gko::read_binary_raw<ValueType, gko::int64>(ois);
+        std::ifstream is(output, std::ios_base::in | std::ios_base::binary);
+        auto data2 = gko::read_binary_raw<ValueType, gko::int64>(is);
         std::cerr << "Comparing against previously read data\n";
         if (data.size != data2.size) {
             throw GKO_STREAM_ERROR("Mismatching sizes!");

benchmark/utils/formats.hpp

@@ -78,8 +78,8 @@ std::string format_description =
     "     Irregular Sparse Matrices.\n"
     "csr: Compressed Sparse Row storage. Ginkgo implementation with\n"
     "     automatic strategy.\n"
-    "csrc: Ginkgo's CSR implementation with automatic stategy.\n"
-    "csri: Ginkgo's CSR implementation with inbalance strategy.\n"
+    "csrc: Ginkgo's CSR implementation with automatic strategy.\n"
+    "csri: Ginkgo's CSR implementation with imbalance strategy.\n"
     "csrm: Ginkgo's CSR implementation with merge_path strategy.\n"
     "csrs: Ginkgo's CSR implementation with sparselib strategy.\n"
     "ell: Ellpack format according to Bell and Garland: Efficient Sparse\n"

benchmark/utils/general.hpp

@@ -179,7 +179,7 @@ void initialize_argument_parsing(int* argc, char** argv[], std::string& header,
 }
 
 /**
- * Print general benchmark informations using the common available parameters
+ * Print general benchmark information using the common available parameters
  *
  * @param extra  describes benchmark specific extra parameters to output
  */

cmake/CTestScript.cmake

@@ -4,7 +4,7 @@
 #
 # Runs our tests through CTest, with support for Coverage or memory checking.
 #
-# This script provides a full CTest run whith result submission to Ginkgo's
+# This script provides a full CTest run with result submission to Ginkgo's
 # CDash dashboard. The supported runs are:
 # + With or without coverage, requires the gcov tool.
 # + With or without address sanitizers.

cmake/Modules/CudaArchitectureSelector.cmake

@@ -119,7 +119,7 @@
 # identifiers in this list will be removed from the list specified by the
 # ``ARCHITECTURES`` list. A warning will be printed for each removed entry.
 # The list also supports aggregates ``All``, ``Auto`` and GPU generation names
-# wich have the same meaning as in the ``ARCHITECTURES'' specification list.
+# which have the same meaning as in the ``ARCHITECTURES'' specification list.
 
 
 if(NOT DEFINED CMAKE_CUDA_COMPILER)

cmake/hip.cmake

@@ -197,7 +197,7 @@ if (GINKGO_HIP_PLATFORM MATCHES "${HIP_PLATFORM_NVIDIA_REGEX}")
     # Remove false positive CUDA warnings when calling one<T>() and zero<T>()
     list(APPEND GINKGO_HIP_NVCC_ADDITIONAL_FLAGS --expt-relaxed-constexpr --expt-extended-lambda)
 
-    if (GINKGO_HIP_PLATFROM MATCHES "${HIP_PLATFORM_NVIDIA_REGEX}"
+    if (GINKGO_HIP_PLATFORM MATCHES "${HIP_PLATFORM_NVIDIA_REGEX}"
             AND CMAKE_CUDA_COMPILER_VERSION MATCHES "9.2"
             AND CMAKE_CUDA_HOST_COMPILER MATCHES ".*clang.*" )
         ginkgo_extract_clang_version(${CMAKE_CUDA_HOST_COMPILER} GINKGO_CUDA_HOST_CLANG_VERSION)

cmake/information_helpers.cmake

@@ -103,7 +103,7 @@ macro(ginkgo_interface_information)
     get_target_property(GINKGO_INTERFACE_LINK_LIBRARIES ginkgo INTERFACE_LINK_LIBRARIES)
     ginkgo_interface_libraries_recursively("${GINKGO_INTERFACE_LINK_LIBRARIES}")
     # Format and store the interface libraries found
-    # remove duplicates on the reversed list to keep the dependecy in the end of list.
+    # remove duplicates on the reversed list to keep the dependency in the end of list.
     list(REVERSE GINKGO_INTERFACE_LIBS_FOUND)
     list(REMOVE_DUPLICATES GINKGO_INTERFACE_LIBS_FOUND)
     list(REVERSE GINKGO_INTERFACE_LIBS_FOUND)

common/cuda_hip/base/executor.hpp.inc

@@ -40,7 +40,7 @@ inline int convert_sm_ver_to_cores(int major, int minor)
     // Defines for GPU Architecture types (using the SM version to determine
     // the # of cores per SM
     typedef struct {
-        int SM;  // 0xMm (hexidecimal notation), M = SM Major version,
+        int SM;  // 0xMm (hexadecimal notation), M = SM Major version,
         // and m = SM minor version
         int Cores;
     } sSMtoCores;

common/cuda_hip/components/segment_scan.hpp.inc

@@ -33,7 +33,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 /**
  * @internal
  *
- * Compute a segement scan using add operation (+) of a subwarp. Each segment
+ * Compute a segment scan using add operation (+) of a subwarp. Each segment
  * performs suffix sum. Works on the source array and returns whether the thread
  * is the first element of its segment with same `ind`.
  */

common/cuda_hip/matrix/csr_kernels.hpp.inc

@@ -606,7 +606,7 @@ __global__ __launch_bounds__(default_block_size) void spgeam(
             }
             // advance by the number of merged elements
             // in theory, we would need to mask by `valid`, but this
-            // would only be false somwhere in the last iteration, where
+            // would only be false somewhere in the last iteration, where
             // we don't need the value of c_begin afterwards, anyways.
             c_begin += popcnt(~prev_equal_mask & lanemask_full);
             return true;

common/cuda_hip/multigrid/pgm_kernels.hpp.inc

@@ -51,9 +51,9 @@ void sort_row_major(std::shared_ptr<const DefaultExecutor> exec, size_type nnz,
     using device_value_type = device_member_type<ValueType>;
     auto vals_it = reinterpret_cast<device_value_type*>(vals);
     auto it = thrust::make_zip_iterator(thrust::make_tuple(row_idxs, col_idxs));
-    // Because reduce_by_key is not determinstic, so we do not need
+    // Because reduce_by_key is not deterministic, so we do not need
     // stable_sort_by_key
-    // TODO: If we have determinstic reduce_by_key, it should be
+    // TODO: If we have deterministic reduce_by_key, it should be
     // stable_sort_by_key
     thrust::sort_by_key(thrust_policy(exec), it, it + nnz, vals_it);
 }

common/unified/multigrid/pgm_kernels.cpp

@@ -135,7 +135,7 @@ void map_row(std::shared_ptr<const DefaultExecutor> exec,
         exec,
         [] GKO_KERNEL(auto tidx, auto fine_row_ptrs, auto agg, auto row_idxs) {
             const auto coarse_row = agg[tidx];
-            // TODO: when it is neccessary, it can use warp per row to improve.
+            // TODO: when it is necessary, it can use warp per row to improve.
             for (auto i = fine_row_ptrs[tidx]; i < fine_row_ptrs[tidx + 1];
                  i++) {
                 row_idxs[i] = coarse_row;
@@ -232,7 +232,7 @@ void find_strongest_neighbor(
                 // all neighbor is agg, connect to the strongest agg
                 // Also, no others will use this item as their
                 // strongest_neighbor because they are already aggregated. Thus,
-                // it is determinstic behavior
+                // it is deterministic behavior
                 agg[row] = agg[strongest_agg];
             } else if (strongest_unagg != -1) {
                 // set the strongest neighbor in the unagg group
@@ -260,7 +260,7 @@ void assign_to_exist_agg(std::shared_ptr<const DefaultExecutor> exec,
 {
     const auto num = agg.get_num_elems();
     if (intermediate_agg.get_num_elems() > 0) {
-        // determinstic kernel
+        // deterministic kernel
         run_kernel(
             exec,
             [] GKO_KERNEL(auto row, auto row_ptrs, auto col_idxs,

core/base/dispatch_helper.hpp

@@ -63,7 +63,7 @@ void run(T, Func, Args...)
  * run uses template to go through the list and select the valid
  * template and run it.
  *
- * @tparam K  the current type tried in the convertion
+ * @tparam K  the current type tried in the conversion
  * @tparam ...Types  the other types will be tried in the conversion if K fails
  * @tparam T  the type of input object
  * @tparam Func  the function will run if the object can be converted to K
@@ -108,7 +108,7 @@ void run(T, Func, Args...)
  *
  * @tparam Base  the Base class with one template
  * @tparam K  the current template type of B. pointer of const Base<K> is tried
- *            in the convertion.
+ *            in the conversion.
  * @tparam ...Types  the other types will be tried in the conversion if K fails
  * @tparam T  the type of input object waiting converted
  * @tparam Func  the function will run if the object can be converted to pointer

core/base/iterator_factory.hpp

@@ -84,7 +84,7 @@ class zip_iterator_reference
     template <std::size_t... idxs>
     value_type cast_impl(std::index_sequence<idxs...>) const
     {
-        // gcc 5 throws error as using unintialized array
+        // gcc 5 throws error as using uninitialized array
         // std::tuple<int, char> t = { 1, '2' }; is not allowed.
         // converting to 'std::tuple<...>' from initializer list would use
         // explicit constructor

core/base/mtx_io.cpp

@@ -267,7 +267,7 @@ class mtx_io {
 
     /**
      * storage modifier hierarchy provides algorithms for handling storage
-     * modifiers (general, symetric, skew symetric, hermitian) and filling the
+     * modifiers (general, symmetric, skew symmetric, hermitian) and filling the
      * entire matrix from the stored parts
      */
     struct storage_modifier {
@@ -491,7 +491,7 @@ class mtx_io {
          * @param os  The output stream to write to
          * @param data  The matrix data to write
          * @param entry_writer  The entry format to write in.
-         * @param modifier  The strorage modifer
+         * @param modifier  The storage modifier
          */
         virtual void write_data(std::ostream& os,
                                 const matrix_data<ValueType, IndexType>& data,
@@ -554,7 +554,7 @@ class mtx_io {
          * @param os  The output stream to write to
          * @param data  The matrix data to write
          * @param entry_writer  The entry format to write in.
-         * @param modifier  The strorage modifer
+         * @param modifier  The storage modifier
          */
         void write_data(std::ostream& os,
                         const matrix_data<ValueType, IndexType>& data,
@@ -623,7 +623,7 @@ class mtx_io {
          * @param os  The output stream to write to
          * @param data  The matrix data to write
          * @param entry_writer  The entry format to write in.
-         * @param modifier  The strorage modifer
+         * @param modifier  The storage modifier
          */
         void write_data(std::ostream& os,
                         const matrix_data<ValueType, IndexType>& data,

core/base/types.hpp

@@ -109,7 +109,7 @@ constexpr std::enable_if_t<(num_groups > current_shift + 1), int> shift(
  *
  * The usage will be the following
  * Set the method with bits Cfg = ConfigSet<b_0, b_1, ..., b_k>
- * Encode the given infomation encoded = Cfg::encode(x_0, x_1, ..., x_k)
+ * Encode the given information encoded = Cfg::encode(x_0, x_1, ..., x_k)
  * Decode the specific position information x_t = Cfg::decode<t>(encoded)
  * The encoded result will use 32 bits to record
  * rrrrr0..01....1...k..k, which 1/2/.../k means the bits store the information

core/solver/gcr.cpp

@@ -186,7 +186,7 @@ void Gcr<ValueType>::apply_dense_impl(const VectorType* dense_b,
     size_type restart_iter = 0;
 
     /* Memory movement summary for average iteration with krylov_dim d:
-     * (4d+22+4/d)n+(d+1+1/d) * values + matrix/preconditioner stroage
+     * (4d+22+4/d)n+(d+1+1/d) * values + matrix/preconditioner storage
      * 1x SpMV:                       2n * values + storage
      * 1x Preconditioner:             2n * values + storage
      * 1x step 1       (scal, axpys)  6n