high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Building-Blocks for Performance Oriented DSLs

Tiark Rompf, Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Programming Languages, Tesla C2050

November 23, 2011 by hgpu

Dynamic adaptation and distribution of binaries to heterogeneous architectures

Espen Angell Kristiansen

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 280, OpenCL, Package, Playstation, Thesis

November 22, 2011 by hgpu

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Chris Gregg, Michael Boyer, Kim Hazelwood, Kevin Skadron

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Tags: ATI, ATI Radeon HD 5870, Computer science, Heterogeneous systems, OpenCL, Performance, Task scheduling

November 22, 2011 by hgpu

Parallelizing Multicore Cache Simulations using Heterogeneous Computing on General Purpose and Graphics Processors

Nikolaos Strikos, Georgios Keramidas, Stefanos Kaxiras

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Tags: Computer science, CUDA, Heterogeneous systems, Memory, nVidia, nVidia GeForce 8800 GTX

November 22, 2011 by hgpu

Experiences with Achieving Portability across Heterogeneous Architectures

Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, OpenMP, Performance

November 22, 2011 by hgpu

Issues in Heterogenenous GPU Clusters

Patrick Carpenter, William Symon

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Tags: Computer science, GPU cluster, Heterogeneous systems, Review

November 21, 2011 by hgpu

Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Fengguang Song, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, BLAS, Computer science, CUDA, Factorization, Heterogeneous systems, nVidia, Tesla M2070

November 20, 2011 by hgpu

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

G.R. Mudalige, M.B. Giles, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 560 Ti, Performance, Tesla C2070

November 19, 2011 by hgpu

Accelerating The Cloud with Heterogeneous Computing

Sahil Suneja, Elliott Baron, Eyal de Lara, Ryan Johnson

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Tags: ATI, ATI Radeon HD 5870, Cloud, Computer science, Hashing, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL

November 18, 2011 by hgpu

Auto-tunable GPU BLAS

Jarle Erdal Steinsland

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Tags: Algorithms, ATI, ATI Radeon HD 5750, ATI Radeon HD 5870, BLAS, Computer science, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Tesla C2070, Thesis

November 18, 2011 by hgpu

The Multi2Sim Simulation Framework: A CPU-GPU Model for Heterogeneous Computing

Rafael Ubal, Julio Sahuquillo, Salvador Petit, Pedro Lopez, Zhongliang Chen, David R. Kaeli

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Tags: ATI, ATI Radeon HD 5870, Computer science, Heterogeneous systems, OpenCL, Operating systems, Package

November 18, 2011 by hgpu

Compilation for Heterogeneous Computing: Automating Analyses, Transformations and Decisions

Serge Guelton, Francois Irigoin, Ronan Keryell

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Tags: Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Quadro FX 2700 M, Package, Signal processing

November 17, 2011 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

Building-Blocks for Performance Oriented DSLs

Dynamic adaptation and distribution of binaries to heterogeneous architectures

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Parallelizing Multicore Cache Simulations using Heterogeneous Computing on General Purpose and Graphics Processors

Experiences with Achieving Portability across Heterogeneous Architectures

Issues in Heterogenenous GPU Clusters

Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

Auto-tunable GPU BLAS

The Multi2Sim Simulation Framework: A CPU-GPU Model for Heterogeneous Computing

Compilation for Heterogeneous Computing: Automating Analyses, Transformations and Decisions

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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