high performance computing on graphics processing units: hgpu.org

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A Performance and Scalability Analysis of the Tsunami Simulation EasyWave for Different Multi-Core Architectures and Programming Models

Steffen Christgau, Johannes Spazier, Bettina Schnor

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Tags: CUDA, Earth and Space Sciences, Geoscience, Intel Xeon Phi, MPI, nVidia, OpenMP, Package, Performance, Tesla K40

May 20, 2015 by hgpu

An Efficient, Automatic Approach to High Performance Heterogeneous Computing

Gordon Inggs, David B. Thomas, Wayne Luk

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Tags: AMD FirePro W5000, ATI, Computational finance, Computer science, Finance, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Grid K2, nVidia Quadro K4000, OpenCL, Option pricing, Performance

May 20, 2015 by hgpu

CHO: Towards a Benchmark Suite for OpenCL FPGA Accelerators

Geoffrey Ndu, Javier Navaridas, Mikel Lujan

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Tags: Benchmarking, Computer science, FPGA, OpenCL, Package, Performance

May 19, 2015 by hgpu

Optimizing Full Correlation Matrix Analysis of fMRI Data on Intel Xeon Phi Coprocessors

Yida Wang, Michael Anderson, Jonathan D. Cohen, Alexander Heinecke, Kai Li, Nadathur Satish, Narayanan Sundaram, Nicholas B. Turk-Browne, Ted Willke

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Tags: Algorithms, Computer science, Intel Xeon Phi, Magnetic resonance imaging, MRI, Performance

May 19, 2015 by hgpu

Efficient Resource Scheduling for Big Data Processing on Accelerator-based Heterogeneous Systems

Ayman Tarakji, David Hebbeker, Lyubomir Georgiev

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Tags: AMD FirePro S10000, AMD FirePro S7000, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Performance, Tesla C2050

May 16, 2015 by hgpu

Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs

George Teodoro, Tahsin Kurc, Guilherme Andrade, Jun Kong, Renato Ferreira, Joel Saltz

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, Performance, Tesla K20

May 16, 2015 by hgpu

Workload Aware Algorithms for Heterogeneous Platforms

Kishore Kothapalli, Sivaramakrishna Indarapu, Shashank Sharma, Dip Sankar Banerjee, Rohit Nigam

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GT 520, nVidia GeForce GTX 580, Performance

May 5, 2015 by hgpu

Fine-Grained Synchronizations and Dataflow Programming on GPUs

Ang Li, Gert-Jan van den Braak, Henk Corporaa, Akash Kumar

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 570, Performance, PTX

May 3, 2015 by hgpu

An efficient midpoint-radius representation format to deal with symmetric fuzzy numbers

Manuel Marin, David Defour, Federico Milano

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Performance

April 20, 2015 by hgpu

Benchmarking the cost of thread divergence in CUDA

Piotr Bialas, Adam Strzelecki

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 610, nVidia GeForce GT 755 M, nVidia GeForce GTX 480, nVidia GeForce GTX 650 M, nVidia GeForce GTX 770, nVidia GeForce GTX 850 M, Performance

April 8, 2015 by hgpu

The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming

Kamran Karimi

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Tags: ATI, Computer science, OpenCL, OpenMP, Performance

March 25, 2015 by hgpu

Raising the Bar for Using GPUs in Software Packet Processing

Anuj Kalia, Dong Zhou, Michael Kaminsky, David G. Andersen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 650 Ti, nVidia GeForce GTX 980, Performance, Tesla K20

March 22, 2015 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

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high performance computing on graphics processing units: hgpu.org

A Performance and Scalability Analysis of the Tsunami Simulation EasyWave for Different Multi-Core Architectures and Programming Models

An Efficient, Automatic Approach to High Performance Heterogeneous Computing

CHO: Towards a Benchmark Suite for OpenCL FPGA Accelerators

Optimizing Full Correlation Matrix Analysis of fMRI Data on Intel Xeon Phi Coprocessors

Efficient Resource Scheduling for Big Data Processing on Accelerator-based Heterogeneous Systems

Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs

Workload Aware Algorithms for Heterogeneous Platforms

Fine-Grained Synchronizations and Dataflow Programming on GPUs

An efficient midpoint-radius representation format to deal with symmetric fuzzy numbers

Benchmarking the cost of thread divergence in CUDA

The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming

Raising the Bar for Using GPUs in Software Packet Processing

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PELSI: Power-Efficient Layer-Switched Inference

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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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