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CS/ECE/ME/EP 759 (High Performance Computing for Engineering Applications) Course Project: Cautiously Aggressive GPU Space Sharing to Improve Resource Utilization and Job Efficiency

Python 32.70% Cuda 0.54% C 0.08% Shell 2.59% TeX 22.49% Jupyter Notebook 41.50% Makefile 0.10%

cs759-sp21's Introduction

CS/ECE/ME/EP 759 Spring 2021 Final Project

This README contains the code base for Rui Pan's final project report: Cautiously Aggressive GPU Space Sharing to Improve Resource Utilization and Job Efficiency.

Some of the prerequisites for replicating the results include:

An NVIDIA GPU with Volta architecture
Python 3.8 nightly build
CUDA-compatible PyTorch & TorchVision

This repo contains:

/data: Source data for running the workloads. It should be set up as follows:
- /imagenet: ImageNet Dataset for resnet50 workloads
- /ml-20m: MovieLens 20M Dataset for recommendation/recoder workloads
- wikitext2: WikiText-2 Dataset for language modeling workloads
/latex: LaTex files for editing the report on Overleaf
/output: Core-specific utilizations of workloads produced using an earlier version of the profiler
/tables: Shell scripts for replicating the profiling results in various tables
/workloads: Common DL/HPC workloads used in the evaluations. A lot of these are copied from Gavel.
plotting.ipynb: Jupyter Notebook that produces all figures in the report
profiler.py: Profiler parser wrapped around nvprof
pymps.py: Provides Python access to NVIDIA CUDA Multi-Process Service (MPS)
README.md: Well, of course I know him. He's me.
report.pdf: PDF version of the final report

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