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Cycle-level, trace-driven, parallel GPU simulator for NVIDIA Pascal.

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GPUcachesim

GPUcachesim is a cycle-level, trace-driven, parallel GPU simulator written in Rust.

As of now, the simulator is validated for the NVIDIA Pascal architecture but extensible to model various hardware configurations.

Project goals
  • provide a modular and extensible simulation framework
  • support for fast, multi-threaded simulation powered by Rust
  • provide pre-configured base configurations for hardware
  • usability-first: we aim to improve UX and DX over existing simulators

Note: GPUcachesim is evolving rapidly at the moment, hence API's and code may undergo large changes in the near future. For that reason, we restrain from publishing versioned packages to https://crates.io just yet. However, it is absolutely possible to clone or fork this repository to try things out.

Try it out

  • Step 0: Build GPUcachesim from source

    git clone https://github.com/romnn/gpucachesim
    cd gpucachesim
    cargo build --release # build the simulator
    cargo build -p trace --release # build the tracer
  • Step 1: Trace an application

    GPUcachesim is a trace-driven simulator, hence we must first trace an input application. Any compiled CUDA application should work!

    TRACES_DIR=./traces/ LD_PRELOAD=./target/release/libtrace.so <executable> [args]

    We do provide a few test applications. Assuming a working CUDA compilation toolchain, you can build our simple vectoradd_l1_enabled application for testing:

    make -Bj -C ./test-apps/vectoradd/
    TRACES_DIR=./traces/ LD_PRELOAD=./target/release/libtrace.so ./test-apps/vectoradd/vectoradd_l1_enabled 100 32
    ls ./traces/ # allocations.json, commands.json, kernel-0.msgpack

    After tracing, the ./traces directory will contain the following files:

    • allocations.json contains a list of traced memory allocations.
    • commands.json contains all traced CUDA commands, such as CUDA memory transfers and kernel launches.
    • kernel-<ID>.msgpack contains the binary encoded instruction trace for each kernel based on its unique kernel launch ID.
  • Step 2: Simulate the trace

    To simulate the traced application, just pass commands.json to GPUcachesim:

    ./target/release/gpucachesim ./traces/commands.json

    To use deterministic parallel simulation, use the --parallel flag. For maximum performance, try --nondeterministic 10. For more available options, see gpucachesim --help.

Contribute

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Acknowledgements

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