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Mpache: Interaction Aware Multi-level Cache Bypassing on GPUs

Mengyue Xi, Tianyu Guo, Xuanteng Huang, Zejia Lin, Xianwei Zhang

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Tags: cache, Compilers, Computer science, nVidia, PTX

March 10, 2025 by hgpu

SUperman: Efficient Permanent Computation on GPUs

Deniz Elbek, Fatih Taşyaran, Bora Uçar, Kamer Kaya

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Tags: Computer science, CUDA, HPC, Numerical Analysis, nVidia, nVidia A100, nVidia Quadro GV100, OpenMPI, Package

March 10, 2025 by hgpu

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

Lingqi Zhang, Jiajun Huang, Sheng Di, Satoshi Matsuoka, Mohamed Wahib

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Tags: Computer science, CUDA, nVidia, nVidia A100, Performance

March 10, 2025 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

Changqing Shi, Yufei Sun, Rui Chen, Jiahao Wang, Qiang Guo, Chunye Gong, Yicheng Sui, Yutong Jin, Yuzhi Zhang

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Tags: Code generation, Computer science, CUDA, HPC, Memory model, nVidia, OpenCL, Package

March 3, 2025 by hgpu

pyATF: Constraint-Based Auto-Tuning in Python

Richard Schulze, Sergei Gorlatch, Ari Rasch

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Tags: Auto-Tuning, Compilers, Computer science, CUDA, nVidia A100, OpenCL, Package, Performance, Python

March 3, 2025 by hgpu

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Jianling Li, Shangzhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, Haojie Wang, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun

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Tags: Benchmarking, Code generation, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, Package, Python

March 3, 2025 by hgpu

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

Radostin Stoyanov, Viktória Spišaková, Jesus Ramos, Steven Gurfinkel, Andrei Vagin, Adrian Reber, Wesley Armour, Rodrigo Bruno

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia RTX A6000, Package, ROCm

March 3, 2025 by hgpu

Towards Studying the Effect of Compiler Optimizations and Software Randomization on GPU Reliability

Pau López Castillón, Xavier Caricchio Hernández, Leonidas Kosmidis

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti

March 3, 2025 by hgpu

Forecasting time series with constraints

Nathan Doumèche, Francis Bach, Éloi Bedek, Gérard Biau, Claire Boyer, Yannig Goude

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Tags: AI, Benchmarking, Computer science, Linear Algebra, Machine learning, nVidia, nVidia L4, Package

February 24, 2025 by hgpu

Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment

Ben Dong, Qian Wang

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Tags: Benchmarking, Cloud, Computer science, LLM, nVidia, nVidia A100, Performance, Security

February 24, 2025 by hgpu

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Robert Tjarko Lange, Aaditya Prasad, Qi Sun, Maxence Faldor, Yujin Tang, David Ha

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Tags: AI, Computer science, CUDA, LLM, nVidia, nVidia H100, Package, Performance

February 24, 2025 by hgpu

KernelBench: Can LLMs Write Efficient GPU Kernels?

Anne Ouyang, Simon Guo, Simran Arora, Alex L. Zhang, William Hu, Christopher Ré, Azalia Mirhoseini

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Tags: AI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia L40s, Package, PyTorch

February 24, 2025 by hgpu

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Mpache: Interaction Aware Multi-level Cache Bypassing on GPUs

SUperman: Efficient Permanent Computation on GPUs

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

Towards Studying the Effect of Compiler Optimizations and Software Randomization on GPU Reliability

Forecasting time series with constraints

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

KernelBench: Can LLMs Write Efficient GPU Kernels?

Recent source codes

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

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