high performance computing on graphics processing units: hgpu.org

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Compiling and Optimizing Java 8 Programs for GPU Execution

Kazuaki Ishizaki, Akihiro Hayashi, Gita Koblents, Vivek Sarkar

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Tags: Code generation, Compilers, Computer science, CUDA, Java, nVidia, OpenCL, Tesla K40

October 27, 2015 by ahayashi

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

Akihiro Hayashi, Kazuaki Ishizaki, Vivek Sarkar, Gita Koblents

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Tags: Code generation, Compilers, Computer science, CUDA, Java, Machine learning, nVidia, OpenCL, Tesla K40

October 27, 2015 by ahayashi

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K40

October 22, 2015 by hgpu

Implementation of a Power Efficient Synthetic Aperture Radar Back Projection Algorithm on FPGAs Using OpenCL

David Fan

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Tags: Algorithms, FPGA, Heterogeneous systems, nVidia, OpenCL, SAR, Signal processing, Tesla K40, Thesis

October 18, 2015 by hgpu

Meta-programming and Multi-stage Programming for GPGPUs

Marc Baboulin, Joel Falcou, Ian Masliah

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Tags: Computer science, CUDA, nVidia, Tesla K40

October 11, 2015 by hgpu

Kinematic Modelling of Disc Galaxies using Graphics Processing Units

Georgios Bekiaris, Karl Glazebrook, Christopher J. Fluke, Roberto Abraham

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Tags: Algorithms, Astrophysics, CUDA, nVidia, Package, Tesla C2070, Tesla K40

October 8, 2015 by hgpu

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kim, Hadi Esmaeilzadeh

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Tags: ARM, ASIC, Computer science, FPGA, Machine learning, nVidia, nVidia GeForce GTX 650 Ti, nVidia Jetson TK1, Performance, Tesla K40

September 29, 2015 by hgpu

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

John-Alexander M. Assael

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Tags: CNN, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Tesla K40, Thesis

September 26, 2015 by hgpu

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Vlad Olaru, Mihai Florea, Cristian Sminchisescu

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Tags: Algorithms, Computer science, Computer vision, CUDA, Matlab, nVidia, nVidia GeForce GTX Titan Black, Package, Tesla K40

September 24, 2015 by hgpu

Adaptive and Transparent Cache Bypassing for GPUs

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

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Tags: Benchmarking, cache, Computer science, Memory, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 570, nVidia GeForce GTX 690, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 980, Performance, PTX, Tesla K40, Tesla K80

September 24, 2015 by hgpu

OpenCL Based Digital Image Projection Acceleration

Bryan Michael Badalamenti

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Tags: Algorithms, Computer science, FPGA, Intel Xeon Phi, OpenCL, Tesla K10, Tesla K40, Thesis

September 24, 2015 by hgpu

CLTune: A Generic Auto-Tuner for OpenCL Kernels

Cedric Nugteren, Valeriu Codreanu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel HD 5100, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Performance, Tesla K40

September 17, 2015 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

Compiling and Optimizing Java 8 Programs for GPU Execution

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Implementation of a Power Efficient Synthetic Aperture Radar Back Projection Algorithm on FPGAs Using OpenCL

Meta-programming and Multi-stage Programming for GPGPUs

Kinematic Modelling of Disc Galaxies using Graphics Processing Units

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Adaptive and Transparent Cache Bypassing for GPUs

OpenCL Based Digital Image Projection Acceleration

CLTune: A Generic Auto-Tuner for OpenCL Kernels

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

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