high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Fast Burrows Wheeler Compression Using CPU and GPU

Aditya Deshpande, P J Narayanan

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, nVidia Quadro FX 3700, Package, Thrust

April 14, 2014 by hgpu

Test-driving Intel Xeon Phi

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

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

April 13, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

Development of High-Performance Software Components for Emerging Architectures

Stefan Lemvig Glimberg, Allan Peter Engsig-Karup, Allan S. Nielsen, Bernd Dammann

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Tags: Computer science, CUDA, GPGPU, iterative methods, Mathematics, multigrid, Package, parallel computing, Partial differential equations, Software development

March 12, 2014 by apek

Genetically Improved CUDA kernels for StereoCamera

W. B. Langdon, M. Harman

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Tags: Computer science, CUDA, Evolutionary Computations, Genetic programming, nVidia, nVidia GeForce GTX 580, Package, Tesla C2050, Tesla K20, Tesla T10

March 12, 2014 by hgpu

Massively parallel read mapping on GPUs with PEANUT

Johannes Koster, Sven Rahmann

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Tags: Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780, OpenCL, Package, Python

March 10, 2014 by hgpu

Converting Data to Task-Parallelism by Rewrites

Ryan R. Newton, Eric Holk, Trevor L. McDonell

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Tags: Computer science, CUDA, Haskell, nVidia, Package, Task scheduling, Tesla C2075

March 7, 2014 by hgpu

Scheduling data flow program in xkaapi: A new affinity based Algorithm for Heterogeneous Architectures

Raphael Bleuse, Thierry Gautier, Joao V. F. Lima, Gregory Mounie, Denis Trystram

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Package, Tesla C2050

February 28, 2014 by hgpu

Optimizing Performance of Stencil Code with SPL Conqueror

Alexander Grebhahn, Norbert Siegmund, Sven Apel, Sebastian Kuckuk, Christian Schmitt, Harald Kostler

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Tags: Computer science, CUDA, Differential equations, Finite difference, Image processing, nVidia, OpenCL, Package, Partial differential equations, PDEs, Tesla K20

February 17, 2014 by hgpu

Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths

Andrew Davidson, Sean Baxter, Michael Garland, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 680, Package

February 15, 2014 by hgpu

Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

Wei Wang, Beng Chin Ooi, Xiaoyan Yang, Dongxiang Zhang, Yueting Zhuang

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Tags: Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX Titan, Package, Python

February 14, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 2014 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

Applications

Fast Burrows Wheeler Compression Using CPU and GPU

Test-driving Intel Xeon Phi

Parallel Support Vector Machines in Practice

Development of High-Performance Software Components for Emerging Architectures

Genetically Improved CUDA kernels for StereoCamera

Massively parallel read mapping on GPUs with PEANUT

Converting Data to Task-Parallelism by Rewrites

Scheduling data flow program in xkaapi: A new affinity based Algorithm for Heterogeneous Architectures

Optimizing Performance of Stencil Code with SPL Conqueror

Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths

Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

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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