high performance computing on graphics processing units: hgpu.org

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Workload Aware Algorithms for Heterogeneous Platforms

Kishore Kothapalli, Sivaramakrishna Indarapu, Shashank Sharma, Dip Sankar Banerjee, Rohit Nigam

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GT 520, nVidia GeForce GTX 580, Performance

May 5, 2015 by hgpu

Fine-Grained Synchronizations and Dataflow Programming on GPUs

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

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 570, Performance, PTX

May 3, 2015 by hgpu

An efficient midpoint-radius representation format to deal with symmetric fuzzy numbers

Manuel Marin, David Defour, Federico Milano

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Performance

April 20, 2015 by hgpu

Benchmarking the cost of thread divergence in CUDA

Piotr Bialas, Adam Strzelecki

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 610, nVidia GeForce GT 755 M, nVidia GeForce GTX 480, nVidia GeForce GTX 650 M, nVidia GeForce GTX 770, nVidia GeForce GTX 850 M, Performance

April 8, 2015 by hgpu

The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming

Kamran Karimi

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Tags: ATI, Computer science, OpenCL, OpenMP, Performance

March 25, 2015 by hgpu

Raising the Bar for Using GPUs in Software Packet Processing

Anuj Kalia, Dong Zhou, Michael Kaminsky, David G. Andersen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 650 Ti, nVidia GeForce GTX 980, Performance, Tesla K20

March 22, 2015 by hgpu

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Weifeng Liu, Brian Vinter

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Source codes

Tags: Algorithms, AMD Radeon R9 290X, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Performance, Sparse matrix

March 20, 2015 by hgpu

The More We Share, The More We Have: Improving GPU performance through Register Sharing

Vishwesh Jatala, Jayvant Anantpur, Amey Karkare

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, Performance

March 20, 2015 by hgpu

An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems

Yong Guo, Ana Lucia Varbanescu, Alexandru Iosup, Dick Epema

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Performance, Tesla K20

March 8, 2015 by hgpu

HPerf: A Lightweight Profiler for Task Distribution on CPU+GPU Platforms

Joo Hwan Lee, Nimit Nigania, Hyesoon Kim, Bevin Brett

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Quadro 2000, OpenCL, Performance

March 8, 2015 by hgpu

Stochastic Gradient Descent on GPUs

Rashid Kaleem, Sreepathi Pai, Keshav Pingali

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Tags: Algorithms, Computer science, Machine learning, nVidia, OpenCL, Performance, Tesla K40

February 22, 2015 by hgpu

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Ang Li, Hammad Mazhar, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenCL, Performance, Python, Sparse matrix, Tesla K20

February 22, 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

Workload Aware Algorithms for Heterogeneous Platforms

Fine-Grained Synchronizations and Dataflow Programming on GPUs

An efficient midpoint-radius representation format to deal with symmetric fuzzy numbers

Benchmarking the cost of thread divergence in CUDA

The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming

Raising the Bar for Using GPUs in Software Packet Processing

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

The More We Share, The More We Have: Improving GPU performance through Register Sharing

An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems

HPerf: A Lightweight Profiler for Task Distribution on CPU+GPU Platforms

Stochastic Gradient Descent on GPUs

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

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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