high performance computing on graphics processing units: hgpu.org

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A Highly Parameterizable Framework for Conditional Restricted Boltzmann Machine Based Workloads Accelerated With FPGAs and OpenCL

Zoran Jaksic, Nicola Cadenelli, David Buchaca Prats, Jorda Polo, Josep Lluis Berral Garcia, David Carrera Perez

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Tags: Computer science, FPGA, HLS, Linear Algebra, Matrix multiplication, Neural networks, OpenCL

November 17, 2019 by hgpu

Code Optimization on GPUs

Changwan Hong

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Tags: Chemistry, Code generation, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla P100, Thesis

November 3, 2019 by hgpu

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

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Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

Philippe Tillet, H. T. Kung, David Cox

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Tags: Compilers, Computer science, CUDA, Deep learning, High-level Languages, LLVM, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1070, Package

June 30, 2019 by hgpu

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Zhen Xie, Guangming Tan, Weifeng Liu, Ninghui Sun

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Tags: Algorithms, Auto-Tuning, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

June 23, 2019 by hgpu

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Dominik Ernst, Georg Hager, Jonas Thies, Gerhard Wellein

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Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Performance, Tesla V100

May 12, 2019 by hgpu

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

James D. Stevens

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Tags: AMD Radeon R9 Fury, ATI, Code generation, Computer science, Finite difference, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 28, 2019 by hgpu

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Hamidreza Khaleghzadeh

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Package, Thesis

March 17, 2019 by hgpu

Supporting mixed-datatype matrix multiplication within the BLIS framework

Field G. Van Zee, Devangi N. Parikh, Robert A. van de Geijn

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Tags: Computer science, Matrix multiplication, OpenMP, Package

January 27, 2019 by hgpu

Performance Evaluation and Tuning of An OpenCL based Matrix Multiplier

Yiyu Tan, Toshiyuki Imamura

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Tags: Computer science, FPGA, Linear Algebra, Matrix multiplication, OpenCL

September 2, 2018 by hgpu

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Tesla V100

September 2, 2018 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Daniel Hanlon, Hamidreza Khalighzadeh, Ravi Reddy Manumachu, Alexey Lastovetsky

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Tags: Cloud, Computer science, CUDA, FPGA, Hybrid computing, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package

August 19, 2018 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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