high performance computing on graphics processing units: hgpu.org

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Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

Benjamin Worpitz

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Tags: Computer science, CUDA, Intel Xeon Phi, OpenMP, Package, Particle-in-cell methods, Performance, performance portability, Tesla K20, Tesla K80, Thesis

April 26, 2016 by hgpu

A Unified, Hardware-Fitted, Cross-GPU Performance Model

James Stevens, Andreas Klockner

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Tags: AMD Radeon R9 Fury, ATI, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 19, 2016 by hgpu

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Yongchao Liu, Srinivas Aluru

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

April 19, 2016 by hgpu

Portable and Transparent Software Managed Scheduling on Accelerators for Fair Resource Sharing

Christos Margiolas, Michael F.P. O'Boyle

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Tags: AMD Radeon R9 295X2, ATI, Computer science, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

April 12, 2016 by hgpu

An Exploratory Study of High Performance Graphics Application Programming Interfaces

Joseph Shiraef

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Tags: AMD Radeon R9 290X, ATI, Computer science, DirectX, nVidia, nVidia GeForce GTX 970, OpenGL, Performance, Thesis, Vulkan

April 6, 2016 by hgpu

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

Zhaokui Li, Jianbin Fang, Tao Tang, Xuhao Chen, Cheng Chen, Canqun Yang

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, Performance

April 3, 2016 by hgpu

A Survey of Recent Prefetching Techniques for Processor Caches

Sparsh Mittal

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Tags: cache, cpu, Hardware, Paper, Performance, Prefetching, Research, Software, survey

March 25, 2016 by sparsh0mittal

Comparison of Technologies for General-Purpose Computing on Graphics Processing Units

Torbjorn Sorman

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Tags: AMD Radeon R7 260X, ATI, Benchmarking, Computer science, DirectCompute, DirectX, nVidia, nVidia GeForce GTX 670, OpenCL, OpenGL, Performance, Thesis

March 22, 2016 by hgpu

A Survey of Techniques for Architecting and Managing GPU Register File

Sparsh Mittal

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Tags: Energy efficiency, GPU, nVidia, Performance, Power, Register file, Reliability, Research, survey

March 22, 2016 by sparsh0mittal

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Li-Wen Chang, Hee-Seok Kim, Wen-mei Hwu

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, Performance, Tesla K20

March 15, 2016 by hgpu

Study and evaluation of an Irregular Graph Algorithm on Multicore and GPU Processor Architectures

Varun Nagpal

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Tesla C1060, Thesis

March 10, 2016 by hgpu

Enhancing productivity and performance portability of OpenCL applications on heterogeneous systems using runtime optimizations

Thibaut Lutz

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Tags: Computer science, nVidia, nVidia GeForce GTX 780, OpenCL, Performance, performance portability, PTX, Thesis

March 8, 2016 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

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high performance computing on graphics processing units: hgpu.org

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

A Unified, Hardware-Fitted, Cross-GPU Performance Model

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Portable and Transparent Software Managed Scheduling on Accelerators for Fair Resource Sharing

An Exploratory Study of High Performance Graphics Application Programming Interfaces

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

A Survey of Recent Prefetching Techniques for Processor Caches

Comparison of Technologies for General-Purpose Computing on Graphics Processing Units

A Survey of Techniques for Architecting and Managing GPU Register File

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Study and evaluation of an Irregular Graph Algorithm on Multicore and GPU Processor Architectures

Enhancing productivity and performance portability of OpenCL applications on heterogeneous systems using runtime optimizations

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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