# gpgpu-sim/gpgpu-sim_distribution

GPGPU-Sim provides a detailed simulation model of contemporary NVIDIA GPUs running CUDA and/or OpenCL workloads.  It includes support for features such as TensorCores and CUDA Dynamic Parallelism as well as a performance visualization tool, AerialVisoin, and an integrated energy model, GPUWattch.

Repository: https://github.com/gpgpu-sim/gpgpu-sim_distribution
Canonical: https://ross.abutalabs.com/products/gpgpu-sim_distribution
Language: C++
License: NOASSERTION
License Family: other
Last push: 2025-02-15T15:54:23+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 6, release rhythm 40, longevity 100
- inputs: {"age_days": 4407, "days_push": 564, "days_rel": 567, "gap_med": 0, "n_releases_24m": 2}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1701, forks 663 (observed 2026-08-28T04:05:24.264973+00:00)

## What it is
GPGPU-Sim is a cycle-level simulator that models contemporary NVIDIA GPUs running CUDA and OpenCL workloads, including support for TensorCores and CUDA Dynamic Parallelism. It bundles the AerialVision performance visualization tool and the AccelWattch/GPUWattch energy model, and integrates with the Accel-Sim trace-based framework.

## Use cases
- simulate CUDA kernels on a modeled GPU without hardware
- run OpenCL workloads on a detailed GPU simulator
- estimate GPU power consumption with AccelWattch
- evaluate GPU microarchitecture design changes
- visualize GPU performance dynamics with AerialVision
- simulate deep learning workloads with Tensor Cores
- run trace-based SASS simulation via Accel-Sim

## When to choose
- you need cycle-accurate GPU architecture research without owning hardware
- you are studying power/performance tradeoffs of NVIDIA GPU designs
- you want to experiment with Tensor Core or dynamic parallelism behavior
- you need a validated, citable GPU simulator for academic publications

## When to avoid
- you just need to run CUDA code fast on real GPUs
- you want a lightweight or functional-only GPU emulator
- you need simulation of non-NVIDIA or modern post-CUDA-11 hardware features out of the box
- you need a maintained production tool rather than a research codebase

## Facets
- artifact type: library
- maturity: maintenance
- function: simulation, benchmarking, gpu-computing, developer-tools
- domain: gpu-computing, hardware, performance
- platform: cpp
- tags: gpu-simulator, cuda, opencl, cycle-level-simulation, power-modeling, accelwattch, aerialvision, academic-research, tensor-cores, accel-sim, research, computer-architecture, linux

## Member repositories
- gpgpu-sim/gpgpu-sim_distribution (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:24.264973+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:37:42.614127+00:00, confidence not recorded.
  - readme: https://github.com/gpgpu-sim/gpgpu-sim_distribution (fetched 2026-08-28T04:05:24.264973+00:00, sha 6bb7f918fa6b)
- Data as of 2026-08-30T08:39:29.467469+00:00.