high performance computing on graphics processing units: hgpu.org

David Abdurachmanov, Kapil Arya, Josh Bendavid, Tommaso Boccali, Gene Cooperman, Andrea Dotti, Peter Elmer, Giulio Eulisse, Francesco Giacomini, Christopher D. Jones, Matteo Manzali, Shahzad Muzaffar

View

Download (PDF)

Tags: ARM, Computational Physics, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, Performance, Physics

November 11, 2013 by hgpu

Performance Analysis of Sobel Edge Detection Filter on GPU using CUDA & OpenGL

Khyati Shah

View

Download (PDF)

Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GT 130, OpenGL, Performance

November 10, 2013 by hgpu

Dynamic autotuning of adaptive fast multipole methods on hybrid multicore CPU & GPU systems

Marcus Holm, Stefan Engblom, Anders Goude, Sverker Holmgren

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Fast multipole method, nVidia, Package, Performance, Tesla M2050

November 6, 2013 by hgpu

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU

George Teodoro, Tahsin Kurc, Jun Kong, Lee Cooper, Joel Saltz

View

Download (PDF)

Tags: Computer science, CUDA, Intel Phi, Intel Xeon Phi, Microscopy, nVidia, OpenMP, Performance, Task scheduling, Tesla K20

November 6, 2013 by hgpu

Parallelizing Alternating Direction Implicit Solver on GPUs

Zhangping Wei, Byunghyun Jang, Yaoxin Zhang, Yafei Jia

View

Download (PDF)

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

November 6, 2013 by hgpu

Performance of Kepler GTX Titan GPUs and Xeon Phi System

Hwancheol Jeong, Weonjong Lee, Jeonghwan Pak, Kwang-jong Choi, Sang-Hyun Park, Jun-sik Yoo, Joo Hwan Kim, Joungjin Lee, Young Woo Lee

View

Download (PDF)

Tags: Computational Physics, CUDA, Intel Phi, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan, Performance, Physics, Tesla K20

November 6, 2013 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Multi-GPU Support on the Marrow Algorithmic Skeleton Framework

Adaptive implementation selection in the SkePU skeleton programming library

Performance and Power Comparisons Between Fermi and Cypress GPUs

SAGE: Self-Tuning Approximation for Graphics Engines

Anatomy of High-Performance Many-Threaded Matrix Multiplication

Performance Evaluation of R with Intel Xeon Phi Coprocessor

Explorations of the Viability of ARM and Xeon Phi for Physics Processing

Performance Analysis of Sobel Edge Detection Filter on GPU using CUDA & OpenGL

Dynamic autotuning of adaptive fast multipole methods on hybrid multicore CPU & GPU systems

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU

Parallelizing Alternating Direction Implicit Solver on GPUs

Performance of Kepler GTX Titan GPUs and Xeon Phi System

Recent source codes

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Most viewed papers (last 30 days)