high performance computing on graphics processing units: hgpu.org

hgpu.org » Memory model

A memory access model for highly-threaded many-core architectures

Lin Ma, Kunal Agrawal, Roger D. Chamberlain

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 480

August 23, 2013 by hgpu

Improving GPU programming models through hardware cache coherence

Inderpreet Singh

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Tags: Computer science, GPGPU-sim, Memory model, Thesis

June 14, 2013 by hgpu

The Hierarchical Memory Machine Model for GPUs

Koji Nakano

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 580

June 12, 2013 by hgpu

Towards shared memory consistency models for GPUs

Tyler Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, Performance, PTX, Thesis

June 4, 2013 by hgpu

Sequential Consistency for Heterogeneous-Race-Free: Programmer-centric Memory Models for Heterogeneous Platforms

Derek R. Hower, Bradford M. Beckmann, Benedict R. Gaster, Blake A. Hechtman, Mark D. Hill, Steven K. Reinhardt, David A. Wood

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, OpenCL, Performance

May 23, 2013 by hgpu

Performance Traps in OpenCL for CPUs

Jie Shen, Jianbin Fang, Henk Sips, Ana Lucia Varbanescu

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Tags: Computer science, Memory model, OpenCL, Performance

March 12, 2013 by hgpu

Efficient Parallel and External Matching

Marcel Birn, Vitaly Osipov, Peter Sanders, Christian Schulz, Nodari Sitchinava

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, MapReduce, Memory model, nVidia, nVidia GeForce GTX 480

February 23, 2013 by hgpu

An Implementation of Conflict-Free Offline Permutation on the GPU

Akihiko Kasagi, Koji Nakano, Yasuaki Ito

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Tags: Computer science, CUDA, Memory, Memory model, nVidia, nVidia GeForce GTX 680, Performance

February 6, 2013 by hgpu

Portable Performance on Heterogeneous Architectures

Phitchaya Mangpo Phothilimthana, Jason Ansel, Jonathan Ragan-Kelley, Saman Amarasinghe

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Tags: Artificial intelligence, ATI, ATI Radeon HD 6630 M, Compilers, Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, OpenCL, Performance, Tesla C2070

February 2, 2013 by hgpu

An Automatic Host and Device Memory Allocation Method for OpenMPC

Hiroaki Uchiyama, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, OpenMP, Performance, Tesla C1060

December 20, 2012 by hgpu

The Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System

Qingyu Meng, Alan Humphrey, Martin Berzins

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, MPI, nVidia, nVidia GeForce GTX 570, Task scheduling, Tesla C2070

November 24, 2012 by hgpu

Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels

Apan Qasem

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Tags: Compilers, Computer science, CUDA, Memory model, nVidia, nVidia GeForce 9800 GT, Tesla C2050

November 8, 2012 by hgpu

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BLOCKBENCH: A Framework for Analyzing Private Blockchains

Blockchain Goes Green? Part II: Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge

hls4ml: Machine learning on FPGAs using HLS

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

GPU-based JSON data processing using structural indexes

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Analytical Performance Estimation during Code Generation on Modern GPUs

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

The Celerity High-level API: C++20 for Accelerator Clusters

GPU-Sched

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

A memory access model for highly-threaded many-core architectures

Improving GPU programming models through hardware cache coherence

The Hierarchical Memory Machine Model for GPUs

Towards shared memory consistency models for GPUs

Sequential Consistency for Heterogeneous-Race-Free: Programmer-centric Memory Models for Heterogeneous Platforms

Performance Traps in OpenCL for CPUs

Efficient Parallel and External Matching

An Implementation of Conflict-Free Offline Permutation on the GPU

Portable Performance on Heterogeneous Architectures

An Automatic Host and Device Memory Allocation Method for OpenMPC

The Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System

Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels

Recent source codes

BLOCKBENCH: A Framework for Analyzing Private Blockchains

hls4ml: Machine learning on FPGAs using HLS

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

GPU-Sched

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