Accel-Sim 2.0 — Validated GPU Simulation with full Hopper support

Accel-Sim is an extensible, validated framework for cycle-level GPU simulation. It traces real SASS execution from NVIDIA hardware with NVBit and replays it on a detailed performance model (GPGPU-Sim 4.x), together with the AccelWattch power model.

Accel-Sim 2.0 adds full NVIDIA Hopper (H100 / H200) support: the Tensor Memory Accelerator (TMA), asynchronous Warp Group MMA (WGMMA), mbarrier producer/consumer synchronization, threadblock clusters, a chiplet / uGPU partitioned memory subsystem, a rebuilt tracer (compressed traces + PyTorch per-layer hooks), and GPUVision for cycle-level hardware correlation. See the full changelog and Table 1 in release.notes.md.

🚀 Simulate contemporary LLM inference and training, end to end

Accel-Sim 2.0 fully supports vLLM inference and PyTorch training — grab any model from Hugging Face, trace inference or a full training step (forward + backward + optimizer) with a few lines of Python, and run it on a cycle-level model of an H100. No hand-written kernels, no unsupported-op workarounds: modern async Hopper kernels (FlashAttention-3, cuBLAS/CUTLASS, NCCL) run out of the box.

And it's accurate. Validated across 34,000+ kernel instances from 22 benchmark suites, Accel-Sim 2.0 achieves a 99% Pearson correlation and just 13.4% mean absolute cycle error against real NVIDIA H100 silicon.

→ Jump to Tracing LLMs with vLLM.

Simulated vs. real H100 across 34,000+ kernels — GPC cycles, warp instructions, and L1/L2 accesses & misses. Each panel shows the Pearson correlation and mean absolute percentage error (MAPE) for that metric.

Roadmap: experimental Blackwell (B200 / RTX 5090) and multi-GPU / NVLink support are incoming shortly.

How to Cite

Cite Accel-Sim cumulatively. If you use anything in this repo, cite the base papers; add the power papers on top only if you use AccelWattch or GPUWattch.

The Accel-Sim 2.0 paper introduces full Hopper support and the 2.0 framework; the ISCA 2020 paper introduces Accel-Sim; the MICRO 2021 paper introduces AccelWattch (see the AccelWattch MICRO'21 Artifact Manual).

Ready-to-use metadata ships with the repo: CITATION.bib (every entry below, copy-pasteable) and CITATION.cff (machine-readable; drives GitHub's "Cite this repository" button in the sidebar). Note that GitHub's button exports only the Accel-Sim 2.0 entry — use CITATION.bib for the full cumulative set.

1. Base — always cite these

Required for any use of Accel-Sim: the tracer, the trace-driven frontend, the tuner, the correlator, the Hopper model — anything.

Accel-Sim 2.0 (2026) ＋ Accel-Sim 1.0 (ISCA'20) ＋ GPGPU-Sim (ISPASS'09)

BibTeX — base

@misc{accelsim2_2026,
  author        = {Junrui Pan and Weili An and Cesar Avalos Baddouh and Christin David Bose and
                   Ni Kang and Aaron Barnes and Ahmad Alawneh and Fangjia Shen and Yechen Liu and
                   Anusuya Nallathambi and Atthin Chandrashekar and Timothy G. Rogers},
  title         = {Architecting the Next Generation of Asynchronous, Distributed {GPUs} for the {AI} Era},
  year          = {2026},
  eprint        = {2608.22602},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AR},
  doi           = {10.48550/arXiv.2608.22602},
  url           = {https://arxiv.org/abs/2608.22602}
}

@inproceedings{accelsim2020,
  author    = {Mahmoud Khairy and Zhesheng Shen and Tor M. Aamodt and Timothy G. Rogers},
  title     = {Accel-Sim: An Extensible Simulation Framework for Validated {GPU} Modeling},
  booktitle = {ISCA},
  year      = {2020},
  doi       = {10.1109/ISCA45697.2020.00047}
}

@inproceedings{gpgpusim2009,
  author    = {Ali Bakhoda and George L. Yuan and Wilson W. L. Fung and Henry Wong and Tor M. Aamodt},
  title     = {Analyzing {CUDA} Workloads Using a Detailed {GPU} Simulator},
  booktitle = {ISPASS},
  year      = {2009},
  doi       = {10.1109/ISPASS.2009.4919648}
}

2. Power modeling — add these if you use AccelWattch or GPUWattch

These are in addition to the base papers above, not instead of them.

＋ AccelWattch (MICRO'21) ＋ GPUWattch (ISCA'13)

BibTeX — power model

@inproceedings{accelwattch2021,
  author    = {Vijay Kandiah and Scott Peverelle and Mahmoud Khairy and Junrui Pan and
               Amogh Manjunath and Timothy G. Rogers and Tor M. Aamodt and Nikos Hardavellas},
  title     = {AccelWattch: A Power Modeling Framework for Modern {GPU}s},
  booktitle = {MICRO},
  year      = {2021},
  doi       = {10.1145/3466752.3480063}
}

@inproceedings{gpuwattch2013,
  author    = {Jingwen Leng and Tayler Hetherington and Ahmed ElTantawy and Syed Gilani and
               Nam Sung Kim and Tor M. Aamodt and Vijay Janapa Reddi},
  title     = {GPUWattch: Enabling Energy Optimizations in {GPGPU}s},
  booktitle = {ISCA},
  year      = {2013},
  doi       = {10.1145/2485922.2485964}
}

Dependencies

This package runs on a modern Linux distro. A prebuilt Docker image is available here (built on nvidia/cuda:12.8.0-cudnn-devel-ubuntu24.04; Dockerfile here).

To build on a local machine (example: Ubuntu 24.04 + CUDA 12.8):

sudo apt-get install -y wget build-essential xutils-dev bison zlib1g-dev flex \
      libglu1-mesa-dev git g++ libssl-dev libxml2-dev libboost-all-dev \
      libzstd-dev vim python3-setuptools python3-pip

pip3 install pyyaml plotly psutil

wget https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda_12.8.1_570.124.06_linux.run
sh cuda_12.8.1_570.124.06_linux.run --silent --toolkit
rm cuda_12.8.1_570.124.06_linux.run

libzstd-dev is required for the compressed .tracez trace format.

Accel-Sim 2.0 uses the GPGPU-Sim 4.x performance model, pulled automatically at build time along with the AccelWattch power model. A companion GPU App Collection provides common benchmarks with build infrastructure for different CUDA versions.

Quick Start

End-to-end: build the simulator, trace an app on real hardware, and simulate it. Every Python script below accepts --help for full options.

1. Build the simulator

pip3 install -r requirements.txt
source ./gpu-simulator/setup_environment.sh

cmake -S ./gpu-simulator/ -B ./gpu-simulator/build
cmake --build ./gpu-simulator/build -j8
cmake --install ./gpu-simulator/build
# Executable: ./gpu-simulator/bin/release/accel-sim.out

2. Get and build some apps

git clone https://github.com/accel-sim/gpu-app-collection
source ./gpu-app-collection/src/setup_environment
make -j -C ./gpu-app-collection/src rodinia_2.0-ft
make    -C ./gpu-app-collection/src data

3. Trace them on a real GPU (produces compressed .tracez traces)

export CUDA_INSTALL_PATH=<your_cuda>
export PATH=$CUDA_INSTALL_PATH/bin:$PATH
./util/tracer_nvbit/install_nvbit.sh
make -C ./util/tracer_nvbit/

./util/tracer_nvbit/run_hw_trace.py -B rodinia_2.0-ft -D <device-num>
# Traces land in ./hw_run/traces/

run_hw_trace.py traces benchmarks declared in the app YAMLs. To trace any other command — your own binary, a Python script, a serving framework — use the run.sh wrapper instead: it sets up the NVBit tracing environment and then execs whatever you hand it.

NVBIT_INSTRUMENTATION_ENABLED=1 TRACES_FOLDER=./my_traces \
    ./util/tracer_nvbit/others/torch_hook/run.sh ./your_cuda_app [args...]

# then post-process the raw traces into .tracez
./util/tracer_nvbit/tracer_tool/traces-processing/post-traces-processing ./my_traces -j 8

The wrapper sets NVBIT_INSTRUMENTATION_ENABLED=0 so PyTorch hooks can switch tracing on for one layer at a time — override it to 1 as above when you want to trace a whole app. TRACES_FOLDER defaults to ./traces.

4. Simulate. Pick a config: H100-SASS / H200-SASS for Hopper, or QV100-SASS, A100-SASS, etc. (see define-standard-cfgs.yml).

./util/job_launching/run_simulations.py \
    -B rodinia_2.0-ft -C H100-SASS \
    -T ./hw_run/traces/device-<device-num>/<cuda-version>/ -N myTest

# Monitor, then collect stats
./util/job_launching/monitor_func_test.py -v -N myTest
./util/job_launching/get_stats.py -N myTest | tee stats.csv

To run a single trace directly (bypassing the launch manager):

./gpu-simulator/bin/release/accel-sim.out \
  -trace ./hw_run/traces/device-<n>/<cuda>/<app>/<args>/traces/kernelslist.g \
  -config ./gpu-simulator/gpgpu-sim/configs/tested-cfgs/SM90_H100/gpgpusim.config \
  -config ./gpu-simulator/configs/tested-cfgs/SM90_H100/trace.config

We recommend the run_simulations.py launch manager for anything beyond a single run.

For PTX execution-driven mode (no traces required), drop -T and use a *-PTX config:

./util/job_launching/run_simulations.py -B rodinia_2.0-ft -C QV100-PTX -N myTest-PTX

Full Hopper Support

Accel-Sim 2.0 models the asynchronous, warp-specialized, persistent execution style of modern Hopper kernels (FlashAttention-3, cuBLAS/CUTLASS GEMMs, NCCL). These features are enabled automatically by the H100-SASS / H200-SASS configs — you just trace and run.

TMA (Tensor Memory Accelerator): hardware-orchestrated bulk tensor data movement, including bulk/store groups and CGA shared-memory multicast.
WGMMA (async Warp Group MMA): warpgroup commit/wait semantics and variable MMA latency that scales with the N tile dimension.
mbarrier synchronization: producer/consumer coordination with async-proxy fences, remote arrive, and dynamic try_wait / spinloop modeling (see spinloop handling).
Threadblock clusters (Cooperative Groups): cluster-aware CTA scheduling and distributed shared memory.
Memory subsystem: HBM3 / HBM3e timing, the L2 Request Coalescer (LRC), IPOLY+MODULO L2 hashing, and a chiplet / uGPU partitioned L2.

See Table 1 in release.notes.md for the complete baseline-vs-2.0 feature map.

Tracing LLMs with vLLM

Large language models are the primary workload Accel-Sim 2.0 is built for — and they can't be traced the old way. Modern LLMs run inside serving frameworks (vLLM, Hugging Face) rather than as a standalone CUDA binary you can LD_PRELOAD, and a single trace of a full model would be enormous and redundant. Instead, Accel-Sim 2.0 attaches NVBit to the live PyTorch process and traces only the layers you select. Because an LLM is a stack of identical transformer blocks, tracing one representative layer is enough to extrapolate end-to-end performance.

The tool is ./util/tracer_nvbit/others/torch_hook/: a hook module that toggles NVBit instrumentation on and off around the forward pass of a named layer, tagging the resulting trace with that layer's name.

1. Build the tracer and install vLLM

export CUDA_INSTALL_PATH=<your_cuda>
export PATH=$CUDA_INSTALL_PATH/bin:$PATH
./util/tracer_nvbit/install_nvbit.sh
make -C ./util/tracer_nvbit/       # builds tracer_tool.so used by the hook

pip install vllm                   # in your inference environment

2. Instrument your vLLM script

Attach the hook to the model inside each vLLM worker via collective_rpc, before generating. Enable instrumentation only for the layer(s) you care about. This is the core of the bundled, runnable vllm_example.py:

from vllm import LLM, SamplingParams
from torch_hook import TorchModelHookWrapper, hook_nvbit_to_layer
import torch

llm = LLM(model="facebook/opt-125m", enforce_eager=True)

def print_layer_names(model: torch.nn.Module):
    for name, module in model.named_modules():
        print(f"  {name}: {module.__class__.__name__}")

def apply_nvbit_hook(model: torch.nn.Module, layers_to_trace: list[str]):
    hook_wrapper = TorchModelHookWrapper(model)
    for layer in layers_to_trace:
        hook_nvbit_to_layer(hook_wrapper, layer)   # NVBit on only during this layer

# (optional) discover the exact layer names first:
llm.collective_rpc(lambda self: print_layer_names(self.model_runner.model))

# instrument one attention layer, then run inference
layers_to_trace = ["model.decoder.layers.10.self_attn"]
llm.collective_rpc(lambda self: apply_nvbit_hook(self.model_runner.model, layers_to_trace))
outputs = llm.generate(["Hello, my name is"],
                       SamplingParams(temperature=0.8, top_p=0.95))

enforce_eager=True is recommended (disables CUDA Graph).

3. Run it through the wrapper

The run.sh wrapper sets up the NVBit tracing environment and execs the command you give it. It works for any Python or CUDA workload — it just also sets two vLLM variables, which are harmless elsewhere:

./util/tracer_nvbit/others/torch_hook/run.sh python3 \
    util/tracer_nvbit/others/torch_hook/vllm_example.py

It sets, among others:

Variable	Value	Purpose
`CUDA_INJECTION64_PATH`	`tracer_tool.so`	Loads the NVBit tracer into the process
`NVBIT_INSTRUMENTATION_ENABLED`	`0`	Off by default — the hook turns it on per layer
`ALLOW_REG_VAL_TRACING`	`1`	Trace register values (tensor descriptors, `mbarrier`)
`SPINLOCK_HANDLING_MODE`	`2`	`mbarrier` spinloops via `mark_region`
`VLLM_ENABLE_V1_MULTIPROCESSING`	`0`	Single process, required for tracing
`VLLM_ALLOW_INSECURE_SERIALIZATION`	`1`	Allows the `collective_rpc` lambda

4. Post-process and simulate on H100

The tracer writes per-layer traces (tagged by layer name) to its output folder. Post-process them into the compressed .tracez format, then simulate with a Hopper config:

# Compress/finalize the raw traces (see Compressed traces section)
./util/tracer_nvbit/tracer_tool/traces-processing/post-traces-processing <traces-folder> -j 8

# Simulate the traced layer on H100 (point -trace at kernelslist.g in the traces folder)
./gpu-simulator/bin/release/accel-sim.out \
  -config ./gpu-simulator/gpgpu-sim/configs/tested-cfgs/SM90_H100/gpgpusim.config \
  -config ./gpu-simulator/configs/tested-cfgs/SM90_H100/trace.config \
  -trace <traces-folder>/kernelslist.g

Selecting which layers to trace

Use print_layer_names(...) (above) to list every module name, then pass the ones you want to layers_to_trace. A layer name like model.decoder.layers.10.self_attn matches the module path in model.named_modules().
For prefill vs. decode studies, trace the same layer under each phase separately — the kernel mix differs (e.g. FlashAttention-3 LDGSTS-heavy decode vs. compute-bound prefill).
Tracing one middle layer is usually representative; trace a few (early / middle / late) if you want to confirm uniformity.

Any LLM framework or PyTorch model

vLLM is just the example used here — the hook works with any LLM framework and any Python module. For any torch.nn.Module you have direct access to, apply the same hook API without collective_rpc:

from torch_hook import TorchModelHookWrapper, hook_nvbit_to_layer

hook_wrapper = TorchModelHookWrapper(model)
hook_nvbit_to_layer(hook_wrapper, "layer_name_to_trace")

Full API reference (pre/post hooks, NVTX markers, per-name registration) is in the torch_hook README.

Core Components

All Python scripts below accept --help for detailed options.

Tracer

An NVBit tool that generates SASS traces from CUDA applications on real hardware. Source lives in ./util/tracer_nvbit/. Build it:

export CUDA_INSTALL_PATH=<your_cuda>
export PATH=$CUDA_INSTALL_PATH/bin:$PATH
./util/tracer_nvbit/install_nvbit.sh
make -C ./util/tracer_nvbit/

Trace apps (see Quick Start for the full flow):

./util/tracer_nvbit/run_hw_trace.py -B rodinia_2.0-ft -D <device-num>

Traces are written to ./hw_run/traces/ in the compressed .tracez format (see Compressed traces). For tracer internals, read the tracer README.

Spinlock handling. Hopper kernels poll mbarrier phase bits in software spinloops. Capturing every polling iteration would bake an unrepresentative count into the trace, so run_hw_trace.py runs a spinlock-detection pass and marks these regions by default (--spinlock_handling mark_region); the simulator then re-evaluates the wait dynamically. To disable it entirely, pass --spinlock_handling none:

./util/tracer_nvbit/run_hw_trace.py -B rodinia_2.0-ft -D <device-num> --spinlock_handling none

The detection tool lives in ./util/tracer_nvbit/others/spinlock_tool/.

Pre-traced applications. To fetch a repository of pre-collected traces:

./get-accel-sim-traces.py

SASS Frontend and Simulation Engine

The frontend consumes SASS traces and drives the GPGPU-Sim 4.x performance model. Build and run as shown in Quick Start. To understand running the simulator in isolation, read the job_launching README and the gpu-simulator README.

Correlator

Matches, plots, and correlates simulator statistics against real-hardware statistics from profiling tools. First generate hardware output with the scripts in ./util/hw_stats:

./util/hw_stats/run_hw.py -B rodinia_2.0-ft
# Newer cards: add --nsight_profiler --disable_nvprof

A comprehensive hardware profiling suite is available via ./util/hw_stats/get_hw_data.sh. Generate correlatable per-kernel stats, then plot:

./util/job_launching/get_stats.py -R -k -K -B rodinia_2.0-ft -C QV100-SASS | tee per.kernel.stats.csv
./util/plotting/plot-correlation.py -c per.kernel.stats.csv -H ./hw_run/QUADRO-V100/device-0/9.1/

Interactive HTML plots, CSVs, and textual summaries appear under ./util/plotting/correl-html/. For cycle-level correlation (rather than per-kernel aggregates), see GPUVision.

Tuner

Automates configuration-file generation from a microbenchmark suite. Fetch the microbenchmarks, provide an hw_def header describing the target hardware, then run them and the tuner:

./util/tuner/get_ubench.sh            # pull the microbenchmark suite
# add/edit an hw_def header for your card, then build & run the ubench:
export CUDA_VISIBLE_DEVICES=0
./util/tuner/run_all.sh | tee stats.txt
./util/tuner/tuner.py -s stats.txt

This generates a folder (named after the device) with GPGPU-Sim and Accel-Sim configs that model the hardware. Full steps (including the hw_def format) are in the tuner README.

New in 2.0 — Feature Guides

These features introduce new workflows. (Hopper modeling itself — TMA/WGMMA/mbarrier — needs no special steps; see Full Hopper Support. For LLM / PyTorch tracing, see Tracing LLMs with vLLM.)

GPUVision — cycle-level CUPTI profiling

GPUVision samples hardware performance counters as a cycle-level time series (not a single per-kernel aggregate), enabling cycle-level correlation against the simulator. It exposes the Nsight Compute metric set via CUPTI's PM Sampling API and can hook arbitrary CUDA API calls. Tool: ./util/hw_stats/pm_cupti_tools/.

# Build
cd util/hw_stats/pm_cupti_tools && mkdir build && cd build && cmake .. && make && cd ../../../..

# Profile any CUDA app (base metrics only)
./util/hw_stats/pm_cupti_tools/cupti.sh ./your_cuda_app [args...]

# Add metric groups with -m (repeatable): gpc, fbp, hub, dram, nvlink
./util/hw_stats/pm_cupti_tools/cupti.sh -m nvlink -m dram ./your_cuda_app [args...]

# Or profile a benchmark suite with custom metrics
./util/hw_stats/pm_cupti_tools/run_cupti.py -B rodinia_2.0-ft \
    -M "sm__cycles_elapsed.avg,dram__bytes.sum" -K 1

cupti.sh exports the PM-sampling configuration, points CUDA_INJECTION64_PATH at the injection library, and then execs the command you gave it — so it profiles any CUDA program without recompiling it. It always samples a base set — sm__cycles_elapsed.avg, sm__inst_executed.sum, sm__pipe_tensor_cycles_active_realtime.sum — and each -m appends a group on top:

`-m` group	Adds
`gpc` / `fbp` / `hub`	`lts__t_sectors.sum` plus the L2 srcnode read/write percentages for that source
`dram`	DRAM read / write / total sectors
`nvlink`	NVLink RX and TX bytes, plus the DRAM sectors

With no -m, the script prints its usage blurb and profiles with the base metrics only. It also sets:

Variable	`cupti.sh` value	Tool default	Purpose
`CUDA_INJECTION64_PATH`	`build/libpmsampling_injection.so`	—	Loads the PM-sampling injection library (build it first)
`INJECTION_METRICS`	base metrics + any `-m` groups	`sm__cycles_elapsed.avg`	Metrics to sample
`INJECTION_KERNEL_COUNT`	`20`	`10`	Kernels per sampling session before flush
`PM_SAMPLING_INTERVAL_SYSCLK`	`3000`	`200000`	Sysclk ticks between samples — this is the cycle-level resolution
`PM_SAMPLING_MAX_SAMPLES`	`160000`	`16384`	Max samples per session
`PM_SAMPLING_HW_BUFFER_BYTES`	`9388608000` (~9.4 GB)	`1048576`	Device-side sampling buffer

Those five are unconditional exports, so setting them in the environment does not work — the script overwrites whatever you pass in. Change them by editing the script (or a copy of it); -m is the only knob exposed on the command line. The ~9.4 GB hardware buffer in particular needs a large-memory GPU and should be cut down on smaller cards. PM_SAMPLING_CSV_PATH is the exception: it is commented out in the script, so an environment value does survive and chooses where samples are written.

PM_SAMPLING_CSV_PATH=$PWD/pm_samples.csv \
    ./util/hw_stats/pm_cupti_tools/cupti.sh -m dram ./my_gemm 4096

Output CSVs (per-sample metric values + timestamps) go to hw_run/cupti/device-X/...; visualize with the bundled plot.py (see an example cycle-level plot). Discover supported metrics on your GPU via gen_metrics.py. Requires CUDA 12.8+ with CUPTI and compute capability ≥ 7.5. See the pm_cupti_tools README.

Compressed traces (.tracez) and traceDsm

Post-processing emits per-warp zstd-compressed .tracez traces by default (large disk and I/O savings), and the simulator loads them page-by-page so runtime memory stays bounded (~4 GB) regardless of kernel size.

# Post-process a kernelslist directory (called automatically by run_hw_trace.py):
./util/tracer_nvbit/tracer_tool/traces-processing/post-traces-processing <path> [-j N] [--text]
#   default: .tracez  |  --text: legacy plain .traceg  |  -j N: parallel threads

Decode / inspect a .tracez with traceDsm:

make -C ./util/tracer_nvbit/others/traceDsm_tool/

# Convert to simulator-compatible .traceg (written next to the input)
./util/tracer_nvbit/others/traceDsm_tool/traceDsm kernel-1.tracez

# Human-readable, field-annotated dump on stdout
./util/tracer_nvbit/others/traceDsm_tool/traceDsm kernel-1.tracez --annotate

Parallel tracing and simulation

Parallel trace post-processing: pass -j N to post-traces-processing (above).
Parallel per-kernel simulation: run_simulations.py --per-kernel splits a benchmark into one run directory per kernel so kernels simulate concurrently:
```
./util/job_launching/run_simulations.py -B <bench> -C H100-SASS -T <trace-path> \
    -N myTest --per-kernel
```
Kernel filtering: set a kernel-name-filter key in an app's YAML entry (see apps/) to trace/simulate only matching kernels.

AccelWattch Power Model

Enable power modeling in a config with:

-power_simulation_enabled 1
-power_simulation_mode 0   # 0 = SASS_SIM/PTX_SIM, 1 = HW, 2 = HYBRID
-accelwattch_xml_file <filename>.xml

Reports are written to accelwattch_power_report.log (per-kernel) in the run directory. See the AccelWattch MICRO'21 Artifact Manual for full details.

AccelWattch SASS SIM:

./util/job_launching/run_simulations.py -B rodinia_2.0-ft -C GV100-Accelwattch_SASS_SIM \
    -T ./hw_run/traces/device-<device-num>/<cuda-version>/ -N myTest

AccelWattch HW / HYBRID: require per-app hardware counters in a hw_perf.csv in the run directory; pass -a to feed the app name to AccelWattch:

./util/job_launching/run_simulations.py -B rodinia_2.0-ft -a \
    -C GV100-Accelwattch_SASS_HYBRID \
    -T ./hw_run/traces/device-<device-num>/<cuda-version>/ -N myTest

A sample GV100 hw_perf.csv is provided at ./util/accelwattch/accelwattch_hw_profiler/hw_perf.csv. 3. AccelWattch PTX SIM:

./util/job_launching/run_simulations.py -B rodinia_2.0-ft -C GV100-Accelwattch_PTX_SIM -N myTest

Hardware profiler: scripts in ./util/accelwattch/accelwattch_hw_profiler/.
Microbenchmarks & QP solver: ubench in the gpu-app-collection; the MATLAB solver is at ./util/accelwattch/quadprog_solver.m.
SASS→power mapping: gpu-simulator/ISA_Def/accelwattch_component_mapping.h, extendable for new SASS instructions.

Provided AccelWattch configs are listed in define-standard-cfgs.yml.

Name		Name	Last commit message	Last commit date
.claude		.claude
.devcontainer		.devcontainer
.github		.github
docs/img		docs/img
gpu-simulator		gpu-simulator
util		util
.cursor		.cursor
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
AccelWattch.md		AccelWattch.md
CITATION.bib		CITATION.bib
CITATION.cff		CITATION.cff
LICENSE		LICENSE
README.md		README.md
get-accel-sim-traces.py		get-accel-sim-traces.py
mlperf.md		mlperf.md
release.notes.md		release.notes.md
requirements.txt		requirements.txt
short-tests.sh		short-tests.sh

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Accel-Sim 2.0 — Validated GPU Simulation with full Hopper support

🚀 Simulate contemporary LLM inference and training, end to end

Contents

How to Cite

1. Base — always cite these

2. Power modeling — add these if you use AccelWattch or GPUWattch

Dependencies

Quick Start

Full Hopper Support

Tracing LLMs with vLLM

1. Build the tracer and install vLLM

2. Instrument your vLLM script

3. Run it through the wrapper

4. Post-process and simulate on H100

Selecting which layers to trace

Any LLM framework or PyTorch model

Core Components

Tracer

SASS Frontend and Simulation Engine

Correlator

Tuner

New in 2.0 — Feature Guides

GPUVision — cycle-level CUPTI profiling

Compressed traces (.tracez) and traceDsm

Parallel tracing and simulation

AccelWattch Power Model

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Accel-Sim 2.0 — Validated GPU Simulation with full Hopper support

🚀 Simulate contemporary LLM inference and training, end to end

Contents

How to Cite

1. Base — always cite these

2. Power modeling — add these if you use AccelWattch or GPUWattch

Dependencies

Quick Start

Full Hopper Support

Tracing LLMs with vLLM

1. Build the tracer and install vLLM

2. Instrument your vLLM script

3. Run it through the wrapper

4. Post-process and simulate on H100

Selecting which layers to trace

Any LLM framework or PyTorch model

Core Components

Tracer

SASS Frontend and Simulation Engine

Correlator

Tuner

New in 2.0 — Feature Guides

GPUVision — cycle-level CUPTI profiling

Compressed traces (.tracez) and traceDsm

Parallel tracing and simulation

AccelWattch Power Model

About

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