high performance computing on graphics processing units: hgpu.org

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Efficient Heterogeneous Execution on Large Multicore and Accelerator Platforms: Case Study Using a Block Tridiagonal Solver

Alfred J. Park, Kalyan S. Perumalla

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Tags: Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Tesla X2090

August 20, 2013 by hgpu

Programming Dense Linear Algebra Kernels on Vectorized Architectures

Jonathan Lawrence Peyton

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Tags: Algorithms, Computer science, Intel Phi, Linear Algebra, Thesis

August 15, 2013 by hgpu

A note on the GPU acceleration of eigenvalue computations

K. Rupp, Ph. Tillet, B. F. Smith, T. Grasser, A. Jungel

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 470, OpenCL

August 1, 2013 by hgpu

Portable HPC Programming on Intel Many-Integrated-Core Hardware with MAGMA Port to Xeon Phi

Jack Dongarra, Mark Gates, Azzam Haidar, Yulu Jia, Khairul Kabir, Piotr Luszczek, Stanimire Tomov

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Tags: Computer science, Heterogeneous systems, Intel Phi, Linear Algebra, Package, Programming techniques

July 30, 2013 by hgpu

Scalable Dense Linear Algebra on Heterogeneous Hardware

George Bosilca, Aurelien Bouteiller, Anthony Danalis, Thomas Herault, Jakub Kurzak, Piotr Luszczek, Stanimire Tomov, Jack J. Dongarra

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 280, Tesla C2050

July 30, 2013 by hgpu

On Benchmarking the Matrix Multiplication Algorithm using OpenMP, MPI and CUDA Programming Languages

Muhammed Al-Mulhem, Abdulah AlDhamin, Raed Al-Shaikh

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Tags: Algorithms, Benchmarking, Bioinformatics, Computer science, CUDA, Linear Algebra, Matrix multiplication, MPI, nVidia, nVidia GeForce GTX 460

July 19, 2013 by hgpu

Hydrodynamic Computation with Hybrid Programming on CPU-GPU Clusters

Tingxing Dong, Veselin Dobrev, Tzanio Kolev, Robert Rieben, Stanimire Tomov, Jack Dongarra

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Tags: CUDA, FEM, Finite element method, Fluid dynamics, GPU cluster, Linear Algebra, MPI, nVidia, Tesla M2050, Tesla M2090

July 15, 2013 by hgpu

Towards Performance-Portable, Scalable, and Convenient Linear Algebra

Philippe Tillet, Karl Rupp, Siegfried Selberherr, Chin-Teng Lin

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Tags: ATI, ATI Radeon HD 7970, Computer science, Linear Algebra, nVidia, nVidia GeForce GTX 470, OpenCL, Task scheduling

July 1, 2013 by hgpu

Composing multiple StarPU applications over heterogeneous machines: a supervised approach

A.-E Hugo A. Guermouche P.-A. Wacrenier R. Namyst

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Tesla M2070

May 23, 2013 by hgpu

Solving Linear Equations with Conjugate Gradient Method on OpenCL Platforms

Caner Sayin

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Tags: Computer science, Heterogeneous systems, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla C2050, Tesla C2070, Thesis

May 20, 2013 by hgpu

CUDA implementation of the solution of a system of linear equations arising in an hp-Finite Element code

Javier Oses Villanueva

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Tags: Algorithms, Computer science, CUDA, Differential equations, Finite element method, Laplace and Poisson equation, Linear Algebra, nVidia, Thesis

May 15, 2013 by hgpu

Exploration of Multifrontal Method with GPU in Power Flow Computation

Xue Li, Fangxing Li, Joshua M. Clark

Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla M2070

May 4, 2013 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Efficient Heterogeneous Execution on Large Multicore and Accelerator Platforms: Case Study Using a Block Tridiagonal Solver

Programming Dense Linear Algebra Kernels on Vectorized Architectures

A note on the GPU acceleration of eigenvalue computations

Portable HPC Programming on Intel Many-Integrated-Core Hardware with MAGMA Port to Xeon Phi

Scalable Dense Linear Algebra on Heterogeneous Hardware

On Benchmarking the Matrix Multiplication Algorithm using OpenMP, MPI and CUDA Programming Languages

Hydrodynamic Computation with Hybrid Programming on CPU-GPU Clusters

Towards Performance-Portable, Scalable, and Convenient Linear Algebra

Composing multiple StarPU applications over heterogeneous machines: a supervised approach

Solving Linear Equations with Conjugate Gradient Method on OpenCL Platforms

CUDA implementation of the solution of a system of linear equations arising in an hp-Finite Element code

Exploration of Multifrontal Method with GPU in Power Flow Computation

Recent source codes

CFAL-bench

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

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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