high performance computing on graphics processing units: hgpu.org

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Power-Efficient Accelerators for High-Performance Applications

Ganesh Suryanarayan Dasika

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Tags: Computer science, Energy-efficient computing, Performance, Thesis

October 2, 2011 by hgpu

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

Christopher J. Rossbach, Jon Currey, Mark Silberstein, Baishakhi Ray, Emmett Witchel

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Tags: Computer science, CUDA, HLSL, nVidia, nVidia GeForce GT 230, nVidia GeForce GTX 470, nVidia GeForce GTX 580, OpenCL, Operating systems, Performance, Programming techniques

October 2, 2011 by hgpu

A Complete Descritpion of the UnPython and Jit4GPU Framework

Rahul Garg, Jose Nelson Amaral

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5850, ATI Stream, Compilers, Computer science, OpenMP, Optimization, Performance, Python

October 2, 2011 by hgpu

Parallel probabilistic model checking on general purpose graphics processors

Dragan Bosnacki, Stefan Edelkamp, Damian Sulewski, Anton Wijs

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, Probabilistic model checking

October 2, 2011 by hgpu

CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems

Aleksandar Ilic, Leonel Sousa

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Tags: Computer science, CUDA, Data parallelism, FFT, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Signal processing

October 2, 2011 by hgpu

Correctly Treating Synchronizations in Compiling Fine-Grained SPMD-Threaded Programs for CPU

Ziyu Guo, Eddy Z. Zhang, Xipeng Shen

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia

October 1, 2011 by hgpu

Optimizing a Semantic Comparator using CUDA-enabled Graphics Hardware

Aalap Tripathy, Suneil Mohan, Rabi Mahapatra

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Tags: Bloom filter, Computer science, CUDA, Green, Hashing, nVidia, Semantic indexing, Tesla C870

October 1, 2011 by hgpu

Customizable Domain-Specific Computing

Jason Cong, Glenn Reinman, Alex Bui, Vivek Sarkar

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Tags: Computer science, FPGA, nVidia, Performance, Programming techniques, Tesla C1060

October 1, 2011 by hgpu

CheCL: Transparent Checkpointing and Process Migration of OpenCL Applications

Hiroyuki Takizawa, Kentaro Koyama, Katuto Sato, Kazuhiko Komatsu, Hiroaki Kobayashi

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, Computer science, MPI, nVidia, OpenCL, Tesla C1060

October 1, 2011 by hgpu

A Comprehensive Performance Comparison of CUDA and OpenCL

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Cell processor, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Optimization, Performance

October 1, 2011 by hgpu

Accelerating Vector Calculations on GPU

Karol Grondzak, Penka Martincova, Ondrej Such

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Tesla C1060, Tutorial

October 1, 2011 by hgpu

Exploring The Latency and Bandwidth Tolerance of CUDA Applications

Gregory Diamos, Sudnya Padalikar

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, Memory, nVidia, Performance, PTX, Tesla C1060

September 30, 2011 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

Power-Efficient Accelerators for High-Performance Applications

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

A Complete Descritpion of the UnPython and Jit4GPU Framework

Parallel probabilistic model checking on general purpose graphics processors

CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems

Correctly Treating Synchronizations in Compiling Fine-Grained SPMD-Threaded Programs for CPU

Optimizing a Semantic Comparator using CUDA-enabled Graphics Hardware

Customizable Domain-Specific Computing

CheCL: Transparent Checkpointing and Process Migration of OpenCL Applications

A Comprehensive Performance Comparison of CUDA and OpenCL

Accelerating Vector Calculations on GPU

Exploring The Latency and Bandwidth Tolerance of CUDA Applications

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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