high performance computing on graphics processing units: hgpu.org

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Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

Genghan Zhang, Yuetong Zhao, Yanting Tao, Zhongming Yu, Guohao Dai, Sitao Huang, Yuan Wen, Pavlos Petoumenos, Yu Wang

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Package, Sparse matrix, Tesla V100

September 11, 2022 by hgpu

Securing GPU via Region-based Bounds Checking

Jaewon Lee, Yonghae Kim, Jiashen Cao, Euna Kim, Jaekyu Lee, Hyesoon Kim

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080, OpenCL, Security

June 19, 2022 by hgpu

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Wei Sun, Ang Li, Tong Geng, Sander Stuijk, Henk Corporaal

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Tags: Benchmarking, Computer science, Matrix multiplication, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, PTX, Sparse matrix

June 12, 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

PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining

Alessandro Berti, Minh Phan Nghia, Wil M.P. van der Aalst

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Tags: Computer science, Databases, nVidia, nVidia GeForce RTX 2080, Package, Python

April 17, 2022 by hgpu

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

Guohao Dai, Guyue Huang, Shang Yang, Zhongming Yu, Hengrui Zhang, Yufei Ding, Yuan Xie, Huazhong Yang, Yu Wang

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Tags: Algorithms, CUDA, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Package, Sparse matrix, Tesla V100

February 20, 2022 by hgpu

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Bo Qiao

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Tags: Code generation, Computer science, CUDA, Image processing, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce RTX 2080, OpenCL, Package, performance portability, Tesla K20, Thesis

January 2, 2022 by hgpu

An Auto-Programming Approach to Vulkan

Vladimir Frolov, Vadim Sanzharov, Vladimir Galaktionov, Alexandr Scherbakov

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Tags: 3D Graphics and Realism, Code generation, nVidia, nVidia GeForce RTX 2080, Raytracing, Vulkan

December 5, 2021 by hgpu

Block Conjugate Gradient Solver in OpenCL

Kert Tali, Eero Vainikko

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Tags: Algorithms, Computer science, Conjugate gradient solver, Linear Algebra, nVidia, nVidia GeForce RTX 2080, OpenCL

July 11, 2021 by hgpu

Using hardware performance counters to speed up autotuning convergence on GPUs

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, OpenCL, Package, Performance, Vulkan

February 23, 2021 by hgpu

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 2021 by hgpu

AZP: Automatic Specialization for Zero Values in Gaming Applications

Mark W. Stephenson, Ram Rangan

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Tags: Computer science, Games, HLSL, nVidia, nVidia GeForce RTX 2080, OpenGL, Performance, Vulkan

November 29, 2020 by hgpu

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

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high performance computing on graphics processing units: hgpu.org

Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

Securing GPU via Region-based Bounds Checking

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

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

PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

An Auto-Programming Approach to Vulkan

Block Conjugate Gradient Solver in OpenCL

Using hardware performance counters to speed up autotuning convergence on GPUs

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

AZP: Automatic Specialization for Zero Values in Gaming Applications

Recent source codes

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

pplx-garden: Perplexity open source garden for inference technology

LC Framework

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