high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

CuPBoP: CUDA for Parallelized and Broad-range Processors

Ruobing Han, Jun Chen, Bhanu Garg, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, HIP, MPI, nVidia, nVidia GeForce GTX 1660 Ti, OpenMP

June 19, 2022 by hgpu

On the Compilation Performance of Current SYCL Implementations

Peter Thoman, Facundo Molina Heredia, Thomas Fahringer

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Tags: Code generation, Compilers, Computer science, Heterogeneous systems, OpenCL, Package, Performance, SYCL

June 12, 2022 by hgpu

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Felipe A. Quezada, Cristóbal A. Navarro, Miguel Romero, Cristhian Aguilera

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce Titan V, nVidia Jetson NX, nVidia Titan RTX, Performance

June 12, 2022 by hgpu

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Andy Adinets, Duane Merrill

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia A100, Package, Performance, Sorting

June 12, 2022 by hgpu

Portable, Scalable Approaches for Improving Asynchronous Many-Task Runtime Node Use

John Keith Holmen

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX Titan X, performance portability, Task scheduling

June 5, 2022 by hgpu

User’s needs influencing HPC technologies

E. Athanasaki, N. Meyer, M. Cestari, A.Tuncer Durak, A. Tekin, P. Gschwandtner

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Tags: AI, Artificial intelligence, Cloud, Computer science, CUDA, HIP, HPC, nVidia, OpenCL, Security, SYCL

May 29, 2022 by hgpu

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

Nicolas Weber

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Tags: AI, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080, Performance

May 29, 2022 by hgpu

Fault Injection techniques for GPU Reliability Evaluation

Luigi Galasso

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Tags: Computer science, CUDA, Fault simulation, nVidia, Thesis

May 29, 2022 by hgpu

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

Tao Ge, Heming Xia, Xin Sun, Si-Qing Chen, Furu Wei

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Tags: Computer science, CUDA, Machine learning, NLP, nVidia, Package, Tesla V100

May 29, 2022 by hgpu

GPU Ray Tracing with Monte Carlo Methods

Daniele Alma

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Tags: 3D Graphics and Realism, Algorithms, nVidia, nVidia GeForce GTX 930 MX, OpenCL, Raytracing, Rendering, Thesis

May 22, 2022 by hgpu

The Application of AI Technology in GPU Scheduling Algorithm Optimization

Zhancai Yan, Yaqiu Liu, Hongrun Shao

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Tags: Artificial intelligence, Computer science, CUDA, GPU cluster, nVidia, Task scheduling

May 22, 2022 by hgpu

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

André Müller, Bertil Schmidt, Richard Membarth, Roland Leißa, Sebastian Hack

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Tags: AMD Radeon Instinct MI100, ATI, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, Sequence alignment, Tesla A100

May 22, 2022 by hgpu

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

CuPBoP: CUDA for Parallelized and Broad-range Processors

On the Compilation Performance of Current SYCL Implementations

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Portable, Scalable Approaches for Improving Asynchronous Many-Task Runtime Node Use

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

Fault Injection techniques for GPU Reliability Evaluation

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

GPU Ray Tracing with Monte Carlo Methods

The Application of AI Technology in GPU Scheduling Algorithm Optimization

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

Recent source codes

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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