high performance computing on graphics processing units: hgpu.org

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A Study of Productivity and Performance of Modern Vector Processors

Paul Springer

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Tags: Computer science, CUDA, nVidia, nVidia Quadro FX 6000, OpenCL, Performance, Thesis

April 9, 2012 by hgpu

Bound the Peak Performance of SGEMM on GPU with software-controlled fast memory

Junjie Lai, Andre Seznec

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Tags: Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 580, Performance

April 7, 2012 by hgpu

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Jing Wu, Joseph JaJa

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C2050

March 30, 2012 by hgpu

Performance evaluation of GPU memory hierarchy using the FFT

Jacobo Lobeiras, Margarita Amor, Ramon Doallo

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Tags: Benchmarking, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Signal processing

March 30, 2012 by hgpu

Auto-tuning a High-Level Language Targeted to GPU Codes

Scott Grauer-Gray, Lifan Xu, Robert Searles, Sudhee Ayalasomayajula, John Cavazos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 280, OpenCL, Optimization, Performance, Programming techniques, Stereo vision, Tesla C2050

March 28, 2012 by hgpu

Improving Performance of OpenCL on CPUs

Ralf Karrenberg, Sebastian Hack

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Tags: Code generation, Computer science, Data parallelism, OpenCL, Performance

March 27, 2012 by hgpu

OpenMPC: Extended OpenMP for Efficient Programming and Tuning on GPUs

Seyong Lee, Rudolf Eigenmann

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, OpenMP, Performance, Programming techniques

March 26, 2012 by hgpu

Impact of asynchronism on GPU accelerated parallel iterative computations

Sylvain Contassot-Vivier, Thomas Jost, Stephane Vialle

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Partial differential equations, PDEs, Performance

March 22, 2012 by hgpu

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Weizhi Xu, Zhiyong Liu, Dongrui Fan, Shuai Jiao, Xiaochun Ye, Fenglong Song, Chenggang Yan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 295, Performance, Sparse matrix

March 21, 2012 by hgpu

Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

Ahmed H. El Zein, Alistair P. Rendell

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla S2050

March 19, 2012 by hgpu

Towards user transparent parallel multimedia computing on GPU-clusters

Ben van Werkhoven, Jason Maassen, Frank J. Seinstra

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Tesla M1060

March 18, 2012 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

A Study of Productivity and Performance of Modern Vector Processors

Bound the Peak Performance of SGEMM on GPU with software-controlled fast memory

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Performance evaluation of GPU memory hierarchy using the FFT

Auto-tuning a High-Level Language Targeted to GPU Codes

Improving Performance of OpenCL on CPUs

OpenMPC: Extended OpenMP for Efficient Programming and Tuning on GPUs

Impact of asynchronism on GPU accelerated parallel iterative computations

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

Towards user transparent parallel multimedia computing on GPU-clusters

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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