high performance computing on graphics processing units: hgpu.org

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Compilers, Computer science, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX Titan X, Optimization, Package, performance portability

October 13, 2018 by hgpu

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Swen Boehm, Swaroop Pophale, Veronica G. Vergara Larrea, Oscar Hernandez

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenACC, OpenCL, OpenMP, performance portability, Tesla K20, Tesla V100

September 23, 2018 by hgpu

EngineCL: Usability and Performance in Heterogeneous Computing

Raul Nozal, Jose Luis Bosque, Ramon Beivide

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Tags: AMD Radeon R7, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, performance portability, Tesla K20

May 12, 2018 by hgpu

HPVM: Heterogeneous Parallel Virtual Machine

Maria Kotsifakou, Prakalp Srivastava, Matthew D. Sinclair, Rakesh Komuravelli, Vikram Adve, Sarita Adve

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability, Virtualization

March 17, 2018 by hgpu

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, performance portability, TensorFlow, Tesla K80

February 15, 2018 by hgpu

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Compilers, Heterogeneous systems, Image processing, Machine learning, nVidia, nVidia GeForce GTX 960, OpenCL, Package, performance portability, Tesla K40

January 13, 2018 by hgpu

High Performance Stencil Code Generation with Lift

Bastian Hagedorn, Larisa Stoltzfus, Michel Steuwer, Sergei Gorlatch, Christophe Dubach

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Tags: ARM, ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Linear Algebra, nVidia, OpenCL, Package, performance portability, Scala, Tesla K20

January 13, 2018 by hgpu

TensorFlow.js: A WebGL accelerated JavaScript library for training and deploying ML models

TensorFlow.js: Machine Learning for the Web and Beyond

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

SHA3-ON-GPU: SHA3 implementation on CPU & GPU

GPU Accelerated Keccak (SHA3) Algorithm

SG++: a numerical library for adaptive Sparse Grids

Heterogeneous Distributed Big Data Clustering on Sparse Grids

ThunderGBM: Fast GBDTs and Random Forests on GPUs

High-performance implementations for counting collisions among objects in 3D space

High Performance Algorithms for Counting Collisions and Pairwise Interactions

BLIS: BLAS-like Library Instantiation Software Framework

Supporting mixed-datatype matrix multiplication within the BLIS framework

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms

Exploring FPGA-specific Optimizations for Irregular OpenCL Applications

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

MapD Core database

Exact Selectivity Computation for Modern In-Memory Database Query Optimization

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7th International Workshop on OpenCL, 2019

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Boston, MA, USA

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

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Larnaca, Cyprus

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

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26-28

Rome, Italy

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

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26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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high performance computing on graphics processing units: hgpu.org

Heterogeneous Distributed Big Data Clustering on Sparse Grids

Efficient Implementation and Optimization of Geometric Multigrid Operations in the LIFT Framework

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Performance Portability Challenges for Fortran Applications

OpenCL Performance Prediction using Architecture-Independent Features

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

EngineCL: Usability and Performance in Heterogeneous Computing

HPVM: Heterogeneous Parallel Virtual Machine

TVM: End-to-End Optimization Stack for Deep Learning

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

High Performance Stencil Code Generation with Lift

Recent source codes

TensorFlow.js: A WebGL accelerated JavaScript library for training and deploying ML models

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

SHA3-ON-GPU: SHA3 implementation on CPU & GPU

SG++: a numerical library for adaptive Sparse Grids

ThunderGBM: Fast GBDTs and Random Forests on GPUs

High-performance implementations for counting collisions among objects in 3D space

BLIS: BLAS-like Library Instantiation Software Framework

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

MapD Core database

Most viewed papers (last 30 days)

Featured events

7th International Workshop on OpenCL, 2019

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

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

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

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019