high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

A Non-linear GPU Thread Map for Triangular Domains

Cristobal A. Navarro, Benjamin Bustos, Nancy Hitschfeld

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 765 M, Performance, Tesla K40

September 8, 2016 by hgpu

A Survey of Techniques for Architecting Processor Components using Domain Wall Memory

Sparsh Mittal

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Tags: cache, cpu, energy, GPU, main memory, Performance, Register file, Reliability

September 5, 2016 by sparsh0mittal

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

A Comprehensive Performance Analysis of HSA and OpenCL 2.0

Saoni Mukherjee, Yifan Sun, Paul Blinzer, Amir Kavyan Ziabari, David Kaeli

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Tags: Algorithms, ATI, Benchmarking, Computer science, Heterogeneous systems, OpenCL, Performance

August 8, 2016 by hgpu

A survey of sparse matrix-vector multiplication performance on large matrices

Max Grossman, Christopher Thiele, Mauricio Araya-Polo, Florian Frank, Faruk O. Alpak, Vivek Sarkar

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Tags: Computer science, CUDA, Linear Algebra, Numerical Analysis, nVidia, Performance, Sparse matrix, survey, Tesla M2070

August 4, 2016 by hgpu

Automatic Loop Partitioning for Heterogeneous Systems

Alexander Baier

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan Black, Performance, PTX, Thesis

August 1, 2016 by hgpu

Towards Predictable Real-Time Performance on Multi-Core Platforms

Hyoseung Kim

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Tags: Computer science, Performance, Sensing, Thesis, Virtualization

July 30, 2016 by hgpu

An Autotuning Framework for Intel Xeon Phi Platforms

Lefteris Christoforidis

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Tags: Computer science, Intel Xeon Phi, Package, Performance, Python, Thesis

July 30, 2016 by hgpu

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Glenn Volkema, Gaurav Khanna

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Tags: AMD Radeon R9 Fury X, ATI, Benchmarking, Computer science, nVidia, nVidia Tegra TX1, OpenCL, Package, Performance, Tesla K40

July 20, 2016 by hgpu

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix decomposition, Mixed precision, Numerical Analysis, nVidia, Performance, Tesla K20

July 16, 2016 by hgpu

LU, QR, and Cholesky factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi

Azzam Haidar, Stanimire Tomov, Konstantin Arturov, Murat Guney, Shane Story, Jack Dongarra

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Tags: Algorithms, Computer science, Factorization, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, Performance, Task scheduling

July 13, 2016 by hgpu

TTC: A Tensor Transposition Compiler for Multiple Architectures

Paul Springer, Aravind Sankaran, Paolo Bientinesi

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Tags: BLAS, Compilers, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, Mathematical Software, nVidia, nVidia GeForce 840 M, Package, Performance, Tesla K40

July 8, 2016 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

A Non-linear GPU Thread Map for Triangular Domains

A Survey of Techniques for Architecting Processor Components using Domain Wall Memory

Streaming Applications on Heterogeneous Platforms

A Comprehensive Performance Analysis of HSA and OpenCL 2.0

A survey of sparse matrix-vector multiplication performance on large matrices

Automatic Loop Partitioning for Heterogeneous Systems

Towards Predictable Real-Time Performance on Multi-Core Platforms

An Autotuning Framework for Intel Xeon Phi Platforms

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

LU, QR, and Cholesky factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi

TTC: A Tensor Transposition Compiler for Multiple Architectures

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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