high performance computing on graphics processing units: hgpu.org

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SemCache: Semantics-aware Caching for Efficient GPU Offloading

Nabeel AlSaber, Milind Kulkarni

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla C2070

April 19, 2013 by hgpu

Geometric Algebra Computing Technology for Accelerated Processing Units

Patrick Charrier, Dietmar Hildenbrand

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Programming techniques

April 1, 2013 by hgpu

A Discussion of Selected Vienna-Libraries for Computational Science

Karl Rupp, Florian Rudolf, Josef Weinbub

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, OpenCL, Programming techniques, Review

April 1, 2013 by hgpu

Formal Analysis of GPU Programs with Atomics via Conflict-Directed Delay-Bounding

Wei-Fan Chiang, Ganesh Gopalakrishnan, Guodong Li, Zvonimir Rakamaric

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Tags: Computer science, CUDA, nVidia, Programming techniques

March 31, 2013 by hgpu

Specification and Verification of GPGPU Programs using Permission-Based Separation Logic

Marieke Huisman and Matej Mihelcic

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Tags: Computer science, OpenCL, Programming techniques

March 31, 2013 by hgpu

Input-Aware Auto-Tuning for Directive-based GPU Programming

Alberto Magni, Dominik Grewe, Nick Johnson

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Tags: Computer science, nVidia, OpenACC, Performance, Programming techniques, Tesla C2050

March 15, 2013 by hgpu

Morph Algorithms on GPUs

Rupesh Nasre, Martin Burtscher, Keshav Pingali

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Programming techniques, Tesla C2070

March 12, 2013 by hgpu

Accelerating Kernel Density Estimation on the GPU Using the CUDA Framework

Panagiotis D. Michailidis, Konstantinos G. Margaritis

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Programming techniques

March 2, 2013 by hgpu

On Performance of GPU and DSP Architectures for Computationally Intensive Applications

John Faella

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Tags: Algorithms, Computer science, CUDA, DSP, nVidia, nVidia GeForce GT 540 M, Programming techniques, Thesis

March 2, 2013 by hgpu

Automatic Mapping of Stream Programs on Multicore Architectures

Pablo de Oliveira Castro, Stephane Louise, Denis Barthou

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Tags: Computer science, CUDA, nVidia, nVidia Quadro FX 580, Programming techniques

February 28, 2013 by hgpu

Accelerating Dynamic Binary Translation with GPUs

Chung Hwan Kim, Srikanth Manikarnike, Vaibhav Sharma, Eric Eide, Robert Ricci

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Tags: Computer science, CUDA, nVidia, nVidia ION, Programming techniques

February 28, 2013 by hgpu

Offload Compiler Runtime for the Intel Xeon Phi Coprocessor

Chris J. Newburn, Rajiv Deodhar, Serguei Dmitriev, Ravi Murty, Ravi Narayanaswamy, John Wiegert, Francisco Chinchilla, Russell McGuire

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Tags: Compilers, Computer science, Heterogeneous systems, Intel, Intel Phi, Programming techniques

February 18, 2013 by hgpu

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

See all packages

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high performance computing on graphics processing units: hgpu.org

SemCache: Semantics-aware Caching for Efficient GPU Offloading

Geometric Algebra Computing Technology for Accelerated Processing Units

A Discussion of Selected Vienna-Libraries for Computational Science

Formal Analysis of GPU Programs with Atomics via Conflict-Directed Delay-Bounding

Specification and Verification of GPGPU Programs using Permission-Based Separation Logic

Input-Aware Auto-Tuning for Directive-based GPU Programming

Morph Algorithms on GPUs

Accelerating Kernel Density Estimation on the GPU Using the CUDA Framework

On Performance of GPU and DSP Architectures for Computationally Intensive Applications

Automatic Mapping of Stream Programs on Multicore Architectures

Accelerating Dynamic Binary Translation with GPUs

Offload Compiler Runtime for the Intel Xeon Phi Coprocessor

Recent source codes

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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