high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Online Performance Projection for Clusters with Heterogeneous GPUs

Lokendra S. Panwar, Ashwin M. Aji, Jiayuan Mengz, Pavan Balajiy, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Computer science, GPGPU-sim, GPU cluster, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla C1060, Tesla C2050

October 25, 2013 by hgpu

An Empirical Study of Intel Xeon Phi

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu

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Tags: Benchmarking, Computer science, Intel Phi, Intel Xeon Phi, Performance

October 25, 2013 by hgpu

Modeling system for GPU parallel tasks performance simulation

A.Y.Doroshenko, K.A. Zhereb, O.G.Beketov, M.V. Gnynjuk

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Tags: Computer science, CUDA, Heterogeneous systems, Java, nVidia, nVidia GeForce GT 650 M, Performance

October 24, 2013 by hgpu

Concurrent kernel execution on Graphic Processing Units

Adrien Cassagne, Aurelien George, Benjamin Lorendeau, Jean-Charles Papin, Antoine Rougier

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660, nVidia Quadro 4000, OpenCL, Performance, Thesis

October 21, 2013 by hgpu

OpenACC-based Snow Simulation

Magnus Alvestad Mikalsen

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenACC, OpenCL, Performance, Tesla C2070, Tesla K20, Thesis

October 18, 2013 by hgpu

Understanding and Modeling the Synchronization Cost in the GPU Architecture

James T. Letendre

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2075, Thesis

October 17, 2013 by hgpu

Empirical performance modeling of GPU kernels using active learning

Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 470, OpenCL, Performance, Tesla C2050, Tesla K20

October 17, 2013 by hgpu

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Egil Fykse

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Tags: Benchmarking, Computer science, Computer vision, CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 660 Ti, nVidia Quadro 1000 M, OpenCV, Performance, Thesis

October 15, 2013 by hgpu

Performance Analysis of a Large Memory Application on Multiple Architectures

P.M.Kogge

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Tags: Benchmarking, Heterogeneous systems, nVidia, nVidia Tesla, Performance

October 9, 2013 by hgpu

GPU-TLS: An Efficient Runtime for Speculative Loop Parallelization on GPUs

Chenggang Zhang, Guodong Han, Cho-Li Wang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Tesla M2050

September 27, 2013 by hgpu

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Guodong Han, Chenggang Zhang, King Tin Lam, Cho-Li Wang

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Java, nVidia, Performance, Tesla M2050

September 27, 2013 by hgpu

Evaluation of autoparallelization toolkits for commodity graphics hardware

David Williams, Valeriu Codreanu, Po Yang, Baoquan Liu, Feng Dong, Burhan Yasar, Babak Mahdian, Alessandro Chiarini, Xia Zhao, Jos B.T.M. Roerdink

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, OpenACC, Performance

September 24, 2013 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

Online Performance Projection for Clusters with Heterogeneous GPUs

An Empirical Study of Intel Xeon Phi

Modeling system for GPU parallel tasks performance simulation

Concurrent kernel execution on Graphic Processing Units

OpenACC-based Snow Simulation

Understanding and Modeling the Synchronization Cost in the GPU Architecture

Empirical performance modeling of GPU kernels using active learning

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Performance Analysis of a Large Memory Application on Multiple Architectures

GPU-TLS: An Efficient Runtime for Speculative Loop Parallelization on GPUs

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Evaluation of autoparallelization toolkits for commodity graphics hardware

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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