high performance computing on graphics processing units: hgpu.org

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The design and verification of Mumax3

Arne Vansteenkiste, Jonathan Leliaert, Mykola Dvornik, Felipe Garcia-Sanchez, Bartel Van Waeyenberge

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Tags: Computational Physics, CUDA, Image generation, Mesoscale and Nanoscale Physics, nVidia, nVidia GeForce GTX Titan, Package, Physics

July 1, 2014 by hgpu

Performance and Efficiency Analysis of Modern Accelerators: Fine-Grained Parallelism on the Intel Xeon Phi

Robrecht Dewaele

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

June 28, 2014 by hgpu

Speeding up a Video Summarization Approach Using GPUs and Multicore CPUs

Suellen S. de Almeida, Antonio Carlos de Nazare Junior, Arnaldo de Albuquerque Arauujo, Guillermo Camara-Chavez, David Menotti

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 650, Package

June 27, 2014 by hgpu

On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

Konstantinos Krommydas, Wu-chun Feng, Muhsen Owaida, Christos D. Antonopoulos, Nikolaos Bellas

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Tags: APU, ATI, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, OpenCL, Package, Performance

June 26, 2014 by hgpu

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Faiz Khan, Vincent Foley-Bourgon, Sujay Kathrotia, Erick Lavoie, Laurie Hendren

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Java, nVidia, OpenCL, Package, Tesla C2050

June 23, 2014 by hgpu

An efficient parallel algorithm for accelerating computational protein design

Yichao Zhou, Wei Xu, Bruce R. Donald, Jianyang Zeng

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Java, nVidia, Package, Tesla K20

June 23, 2014 by hgpu

Medusa: A Parallel Graph Processing System on Graphics Processors

Jianlong Zhong, Bingsheng He

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Tags: Computer science, CUDA, Graph theory, nVidia, Package, Tesla C2050

June 19, 2014 by hgpu

Synergia CUDA: GPU-accelerated accelerator modeling package

Q. Lu, J. Amundson

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Tags: CUDA, FFT, GPU cluster, MPI, nVidia, Package, Particle-in-cell methods, Physics, Tesla C1070, Tesla K20

June 17, 2014 by hgpu

A Fast Batched Cholesky Factorization on a GPU

Tingxing Dong, Azzam Haidar, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Package, Tesla K40

June 12, 2014 by hgpu

Mapping parallel programs to heterogeneous multi-core systems

Dominik Grewe

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Tags: Algorithms, ATI, ATI Radeon HD 7990, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Thesis

June 5, 2014 by hgpu

Adventures in the microlensing cloud: large datasets, eResearch tools, and GPUs

Georgios Vernardos, Christopher J Fluke

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, Package, Tesla C2070, Tesla M2090

June 5, 2014 by hgpu

Loo.py: transformation-based code generation for GPUs and CPUs

Andreas Klockner

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Tags: ATI, ATI Radeon HD 7990, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, Programming Languages, PyOpenCL, Python

June 2, 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

Packages

The design and verification of Mumax3

Performance and Efficiency Analysis of Modern Accelerators: Fine-Grained Parallelism on the Intel Xeon Phi

Speeding up a Video Summarization Approach Using GPUs and Multicore CPUs

On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

An efficient parallel algorithm for accelerating computational protein design

Medusa: A Parallel Graph Processing System on Graphics Processors

Synergia CUDA: GPU-accelerated accelerator modeling package

A Fast Batched Cholesky Factorization on a GPU

Mapping parallel programs to heterogeneous multi-core systems

Adventures in the microlensing cloud: large datasets, eResearch tools, and GPUs

Loo.py: transformation-based code generation for GPUs and CPUs

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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