high performance computing on graphics processing units: hgpu.org

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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

SolidTextureNets

On Demand Solid Texture Synthesis Using Deep 3D Networks

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

deepnetts: Deep Netts Pure Java Deep Learning Engine

Towards High Performance Java-based Deep Learning Frameworks

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

TACO: The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

OpenCL-FPGA-LLVM: LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

CUDAnative.jl: Julia support for native CUDA programming

Abstractions for Programming Graphics Processors in High-Level Programming Languages

GPU explicit-state model checking

Porting tree-based hash table compression to GPGPU model checking

OCLADock: OpenCL Accelerated Molecular Docking

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

jax-md: Differentiable, Hardware Accelerated, Molecular Dynamics

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

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