Split project into two subprojects (#350)

content/project/osre24/uga/genomicsWF/index.md

@@ -9,21 +9,29 @@ lastmod: 2024-02-05
 
 **Project Idea description**
 
-A thorough understanding of workload is a crucial component in designing high-throughput systems. In the context of genomics workflow, the workloads are often composed of large input files (e.g., tens to hundreds of GB per file) that are processed by a diverse set of applications. Each application has its own resource utilization characteristics, ranging from I/O bound, memory-bound, and compute-bound, up to a combination of them. It is crucial to accurately measure and document these characteristics in order to leverage them for a particular target system.
+We aim to characterize the performance of genomic workflows on HPC clusters by conducting two research activities using a broad set of state-of-the-art genomic applications and open-source datasets.
 
-Such measurement effort is commonly conducted through benchmarking. However, many existing studies for benchmarking genomics workflows tend to be either non-comprehensive or outdated, especially given the rapid innovation in the bioinformatics field. Often, these studies measure only a subset of the resources that are needed (e.g., only compute and memory, but not I/O). Moreover, we found there are surprisingly few such studies in the last years, despite various software/hardware advancements that have been made.
+**Performance Benchmarking and Characterizing Genomic Workflows:**
+- **Topics**: High Performance Computing (HPC), Data Analysis, Scientific Workflows
+- **Skills**: Linux, Python, Bash Scripting, Cloud Computing, Data Science Toolkit (e.g. Numpy, Pandas, SciPy)
+- **Difficulty**: Medium
+- **Size**: Large (350 hours)
+- **Mentor(s):**  {{% mention inkee.kim %}}
 
-We aim to bridge this gap in understanding genomic workflows by conducting a comprehensive characterization of a broad set of genomics applications. Students will have the opportunity to learn genomics data processing using state-of-the-art applications, workflows, and real-world data. They will collect and package datasets for I/O, memory, and compute utilization using industry-standard tools and best practices. Students will also explore to investigate the correlation of input data size/quality to application execution time. Subsequently, they will analyze the dataset and create a performance model for each application. We will make the generated dataset & analysis publicly accessible, given adequate quality. All experiments will also be conducted in a reproducible manner (e.g., as a Trovi package or Chameleon VM images), and all code will be open-sourced (e.g., shared on a public Github repo).
+In this activity, students will perform comprehensive performance measurements of genomic data processing on HPC clusters using state-of-the-art applications, workflows, and real-world datasets. They will collect and package datasets for I/O, memory, and compute utilization using industry-standard tools and best practices. 
 
+**Investigating the Impact of Input Data Quality on the Execution Time of Genomic Workflows:**
+- **Topics**: Machine Learning, Data Analysis, and Scientific Workflows and Computations
+- **Skills**: Linux, Python, Bash Scripting, Cloud Computing, Machine Learning, Data Science Toolkit (e.g. Numpy, Pandas, SciPy)
+- **Difficulty**: Difficult
+- **Size**: Medium (175 hours)
+- **Mentor(s):**  {{% mention inkee.kim %}}
 
-**Project Deliverable**
+In this activity, students will examine the correlation between input data size and quality and their impact on the execution time of various genomic applications. They will then analyze the dataset to create a performance model for genomic applications. We will make the collected dataset & analysis publicly accessible, given adequate quality. 
 
-A Github repository and/or Chameleon VM image containing source code for application executions & metrics collection.
-Jupyter notebooks and/or Trovi artifacts containing analysis and mathematical models for application resource utilization & the effects of data quality. 
+For both subprojects, all experiments will also be conducted in a reproducible manner (e.g., as a Trovi package or Chameleon VM images), and all code will be open-sourced (e.g., shared on a public Github repo).
 
+**Project Deliverable**:
 
-- **Topics:** High Performance Computing (HPC), Performance Modeling, Data Analysis, Scientific Workflows
-- **Skills:** Linux, Python, Bash Scripting, Cloud Computing, Machine Learning, Data Science Toolkit (e.g. Numpy, Pandas, SciPy)
-- **Difficulty:** Difficult
-- **Size:** Large (350 + 175) hours
-- **Mentor(s):**  {{% mention inkee.kim %}}
+A Github repository and/or Chameleon VM image containing source code for application executions & metrics collection.
+Jupyter notebooks and/or Trovi artifacts containing analysis and mathematical models for application resource utilization & the effects of data quality.