CAWS (Cache-locality Based Adaptive Warp Scheduling)

This repository contains the source code for reproducing the experiments in the paper "Cache-locality Based Adaptive Warp Scheduling for Neural Network Acceleration on GPGPUs" in IEEE 35th International System-on-Chip Conference.

This work based on GPGPU-Sim v3.2.0

Prerequisite

You can find GPGPU-Sim v3.2.0 in https://hub.docker.com/r/tgrogers/gpgpu-sim_regress

Environment

• Ubuntu 20.04
• GPGPU-Sim v3.2.0
• CUDA 11.0
• gcc 5.5

Benchmark

The benchmark used in this work.

• ISPASS, https://github.com/gpgpu-sim/ispass2009-benchmarks.git
• Parboil, http://impact.crhc.illinois.edu/parboil/parboil.aspx
• PolyBench, http://web.cs.ucla.edu/~pouchet/software/polybench/
• Rodinia-3.1, https://www.cs.virginia.edu/rodinia/doku.php
• Tango, https://gitlab.com/Tango-DNNbench/Tango/-/tree/master/

Run

Use src to replace the src directory in gpgpu-sim_distribution you download.

Use configs/tested-cfgs/SM2_GTX480/gpgpusim.config when you run GPGPU-Sim. We add mode -gpgpu_scheduler caws.

modified file:

• shader.cc. shader.h
• gpu-sim.cc, gpu-sim.h
• gpu-cache.cc, gpu-cache.h
• configs/tested-cfgs/SM2_GTX480/gpgpusim.config