high performance computing on graphics processing units: hgpu.org

Programming

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

Li-Wen Chang

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, OpenCL, performance portability, Tesla C2050, Tesla K20, Thesis

October 15, 2017 by hgpu

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

Satya P. Jammy, Christian T. Jacobs, David J. Lusher, Neil D. Sandham

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Finite difference, Intel Xeon Phi, nVidia, Tesla K40

October 3, 2017 by hgpu

An Efficient Load Balancing Method for Tree Algorithms

Osama Talaat Ibrahim, Ahmed El-Mahdy

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Tags: Algorithms, Computer science, Intel Xeon Phi, load balancing, Performance

October 3, 2017 by hgpu

Computing Treewidth on the GPU

Tom C. van der Zanden, Hans L. Bodlaender

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Source codes

Tags: Algorithms, Bloom filter, Computer science, Graph theory, nVidia, nVidia GeForce GTX 1060, OpenCL, Package

October 3, 2017 by hgpu

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

Dalal Sukkari, Hatem Ltaief, Mathieu Faverge, David Keyes

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Tags: Algorithms, Benchmarking, Computer science, Factorization, Intel Xeon Phi, nVidia, StarPU, Task scheduling, Tesla K80, Tesla P100

September 21, 2017 by hgpu

Distributed Training Large-Scale Deep Architectures

Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, Edward Y. Chang

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Data parallelism, Deep learning, nVidia, Tesla K80

September 21, 2017 by hgpu

Sorting with GPUs: A Survey

Dmitri I. Arkhipov, Di Wu, Keqin Li, Amelia C. Regan

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Tags: Algorithms, Computer science, CUDA, nVidia, Sorting, survey

September 12, 2017 by hgpu

Progressive Clustering of Big Data with GPU Acceleration and Visualization

Jun Wang, Eric Papenhausen, Bing Wang, Sungsoo Ha, Alla Zelenyuk, Klaus Mueller

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Tags: Algorithms, big data, Clustering, Computer science, CUDA, nVidia, Tesla K20, Visualization

September 9, 2017 by hgpu

FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

Yiqiu Wang, Anshumali Shrivastava, Junghee Ryu

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Tags: Algorithms, Computer science, Hashing, Machine learning, Nearest neighbour, nVidia, OpenCL, OpenMP, Tesla P100

September 7, 2017 by hgpu

Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

Viacheslav Khomenko, Oleg Shyshkov, Olga Radyvonenko, Kostiantyn Bokhan

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Tags: Algorithms, Computer science, Deep learning, LSTM, Neural networks, nVidia, Tesla K40, Theano

September 7, 2017 by hgpu

Integer sorting on multicores: some (experiments and) observations

Alexandros V. Gerbessiotis

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Performance, Sorting, Tesla K40

September 3, 2017 by hgpu

Accelerate Local Tone Mapping for High Dynamic Range Images Using OpenCL with GPU

Kuo-Feng Liao, Yarsun Hsu

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Tags: Algorithms, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GT 750 M, OpenCL

August 26, 2017 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

An Efficient Load Balancing Method for Tree Algorithms

Computing Treewidth on the GPU

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

Distributed Training Large-Scale Deep Architectures

Sorting with GPUs: A Survey

Progressive Clustering of Big Data with GPU Acceleration and Visualization

FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

Integer sorting on multicores: some (experiments and) observations

Accelerate Local Tone Mapping for High Dynamic Range Images Using OpenCL with GPU

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)