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Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Suyang Zhu, Sunita Chandrasekaran, Peng Sun, Barbara Chapman, Marcus Winter, Tobias Schuele

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson TK1

August 28, 2016 by hgpu

Benchmarking State-of-the-Art Deep Learning Software Tools

Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu

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Tags: Benchmarking, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, RNN, Tesla K80

August 28, 2016 by hgpu

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Christopher P. Stone, Kyle E. Niemeyer

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Tags: Computational Physics, Computer science, Differential equations, Intel Xeon Phi, nVidia, ODEs, OpenCL, Ordinary differential equations, Physics, Tesla K20

August 28, 2016 by hgpu

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Dennis Bazow, Ulrich W. Heinz, Michael Strickland

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, High Energy Physics - Phenomenology, Intel Xeon Phi, Nuclear Theory, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan Z, Physics

August 28, 2016 by hgpu

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter

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Tags: Algorithms, Artificial intelligence, Bayesian, Computer science, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Optimization, Package, Tesla M2075

August 23, 2016 by hgpu

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Ahmed E. Helal, Paul Sathre, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, Intel Xeon Phi, MPI, nVidia, OpenCL, performance portability, Tesla K20

August 23, 2016 by hgpu

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

Tingxing Dong, Azzam Haidar, Piotr Luszczek, Stanimire Tomov, Ahmad Abdelfattah, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Factorization, Fluid dynamics, Linear Algebra, nVidia, Tesla K40

August 23, 2016 by hgpu

Hybrid CPU-GPU Framework for Network Motifs

Ryan A. Rossi, Rong Zhou

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Tags: Algorithms, Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX Titan Black

August 23, 2016 by hgpu

Streaming Applications on Heterogeneous Platforms

Zhaokui Li, Jianbin Fang, Tao Tang, Xuhao Chen, Canqun Yang

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, Performance, Tesla K80

August 18, 2016 by hgpu

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

GPU-accelerated Gibbs Sampling

Alexander Terenin, Shawfeng Dong, David Draper

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Tags: Bayesian, big data, CUDA, nVidia, Statistics, Tesla K20

August 18, 2016 by hgpu

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

August Ernstsson

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, OpenMP, Programming techniques, Tesla K20, Thesis

August 18, 2016 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

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Benchmarking State-of-the-Art Deep Learning Software Tools

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

Hybrid CPU-GPU Framework for Network Motifs

Streaming Applications on Heterogeneous Platforms

GPU-Acceleration of In-Memory Data Analytics

GPU-accelerated Gibbs Sampling

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

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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