high performance computing on graphics processing units: hgpu.org

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Generalized Resource Allocation for the Cloud

Anshul Rai, Ranjita Bhagwan, Saikat Guha

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Tags: ATI, ATI Radeon HD 6990, Cloud, Computer science, nVidia, OpenCL, Performance, Tesla C2075, Virtualization

October 17, 2012 by hgpu

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Ion Lungu, Dana-Mihaela Petrosanu, Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Optimization, Performance

October 14, 2012 by hgpu

Programming NVIDIA cards by means of transitive closure based parallelization algorithms

Marek Palkowski, Wlodzimierz Bielecki

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Tags: Computer science, CUDA, nVidia, Performance, Tesla S1070

October 13, 2012 by hgpu

Extendable Pattern-Oriented Optimization Directives (extended version)

Huimin Cui, Jingling Xue, Lei Wang, Yang Yang, Xiaobing Feng, Dongrui Fan

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Tags: Compilers, Computer science, nVidia, nVidia GeForce GTX 285, Optimization, Performance

October 13, 2012 by hgpu

Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs

Peng Di, Ding Ye, Yu Su, Yulei Sui, Jingling Xue

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C1060, Tesla C2050

October 13, 2012 by hgpu

A Quantitative Study of Irregular Programs on GPUs

Martin Burtscher, Rupesh Nasre, Keshav Pingali

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 6000, Performance

September 23, 2012 by hgpu

Fast and Efficient Automatic Memory Management for GPUs using Compiler-Assisted Runtime Coherence Scheme

Sreepathi Pai, R. Govindarajan, Matthew J. Thazhuthaveetil

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Tags: Computer science, CUDA, Memory model, nVidia, Performance, Tesla C1060

September 21, 2012 by hgpu

Parallel Benefit on Different Programming Paradigms

Chau-Yi Chou, Sheng-Hsiu Kuo, Chih-Wei Hsieh, Tsung-Che Tsai, Hsi-Ya Chang

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GT 200, OpenMP, Performance

September 16, 2012 by hgpu

Medusa: Simplified Graph Processing on GPUs

Jianlong Zhong, Bingsheng He

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Optimization, Performance, Tesla C2050

September 12, 2012 by hgpu

Performance Improvement of TOUGH2 Simulation with Graphics Processing Unit

Yusuke Shimotoku, Toshiaki Tanaka, Ryuichi Itoi

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C2070

September 10, 2012 by hgpu

Performance Evaluation of Concurrent Lock-free Data Structures on GPUs

Prabhakar Misra, Mainak Chaudhuri

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, Performance, Tesla C2070

September 8, 2012 by hgpu

Can GPGPU Programming Be Liberated from the Data-Parallel Bottleneck?

Benedict R. Gaster, Lee Howes

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Tags: ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, OpenCL, Performance

September 7, 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

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Programming NVIDIA cards by means of transitive closure based parallelization algorithms

Extendable Pattern-Oriented Optimization Directives (extended version)

Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs

A Quantitative Study of Irregular Programs on GPUs

Fast and Efficient Automatic Memory Management for GPUs using Compiler-Assisted Runtime Coherence Scheme

Parallel Benefit on Different Programming Paradigms

Medusa: Simplified Graph Processing on GPUs

Performance Improvement of TOUGH2 Simulation with Graphics Processing Unit

Performance Evaluation of Concurrent Lock-free Data Structures on GPUs

Can GPGPU Programming Be Liberated from the Data-Parallel Bottleneck?

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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