high performance computing on graphics processing units: hgpu.org

Niels Drost, Jason Maassen, Maarten A.J. van Meersbergen, Henri E. Bal, F. Inti Pelupessy, Simon Portegies Zwart, Michael Kliphuis, Henk A. Dijkstra, Frank J. Seinstra

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Tags: Astrophysics, Cloud, Computer science, CUDA, Earth and Space Sciences, Heterogeneous systems, nVidia, Physics, Solar and Stellar Astrophysics, Tesla C2050

March 6, 2012 by hgpu

Benchmarking Next Generation Hardware Platforms: An Experimental Approach

Vishakha Gupta, Adit Ranadive, Ada Gavrilovska, Karsten Schwan

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Tags: Benchmarking, CUDA, Finance, Heterogeneous systems, Image processing, nVidia, nVidia GeForce 8800 GTX

March 1, 2012 by hgpu

Cooperative Heterogeneous Computing for Parallel Processing on CPU/GPU Hybrids

Changmin Lee, Won W. Ro, Jean-Luc Gaudiot

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9400 GT

February 26, 2012 by hgpu

Power-Efficient Time-Sensitive Mapping in Heterogeneous Systems

Cong Liu, Jian Li, Wei Huang, Juan Rubio, Evan Speight, Xiaozhu Lin

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Tags: ATI, ATI Radeon HD 5770, Computer science, Energy-efficient computing, Heterogeneous systems, OpenCL

February 26, 2012 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

Real-time execution of image change detection

Dynamic Compilation of Data-Parallel Kernels for Vector Processors

NUMA Data-Access Bandwidth Characterization and Modeling

Asynchronous Parallel Computing Model of Global Motion Estimation with CUDA

A Study of CUDA Acceleration and Impact of Data Transfer Overhead in Heterogeneous Environment

Dynamic Scheduling for Work Agglomeration on Heterogeneous Clusters

Compiler Assisted Runtime Adaptation

PartialRC: A Partial Recomputing Method for Efficient Fault Recovery on GPGPUs

Benchmarking Next Generation Hardware Platforms: An Experimental Approach

Cooperative Heterogeneous Computing for Parallel Processing on CPU/GPU Hybrids

Power-Efficient Time-Sensitive Mapping in Heterogeneous Systems

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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