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

Ari Rasch, Richard Schulze, Sergei Gorlatch

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Tags: Code generation, Compilers, Computer science, nVidia, OpenCL, Package, performance portability, Tesla V100

January 19, 2020 by hgpu

GPU Tensor Cores for fast Arithmetic Reductions

Cristóbal A. Navarro, Roberto Carrasco, Ricardo J. Barrientos, Javier A. Riquelme, Raimundo Vega

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, Tesla V100, TPU

January 19, 2020 by hgpu

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

Jiajia Li, Mahesh Lakshminarasimhan, Xiaolong Wu, Ang Li, Catherine Olschanowsky, Kevin Barker

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Tags: Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia DGX-1, Package, Performance, Tesla P100, Tesla V100

January 12, 2020 by hgpu

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

Ryan Senanayake, Fredrik Kjolstad, Changwan Hong, Shoaib Kamil, Saman Amarasinghe

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Tags: Code generation, Compilers, Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, Package, Sparse matrix, Tesla V100

January 5, 2020 by hgpu

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

Leonardo Solis Vasquez

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Tags: AMD Radeon RX Vega 56, ATI, Computer science, FPGA, Heterogeneous systems, Macromolecule, nVidia, OpenCL, Package, Physics, Tesla V100, Thesis

December 29, 2019 by hgpu

GPU Computing with Python: Performance, Energy Efficiency and Usability

Håvard H. Holm, André R. Brodtkorb, Martin L. Sætra

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 840 M, OpenCL, Performance, Python, Tesla K20, Tesla M2090, Tesla P100, Tesla V100

December 8, 2019 by hgpu

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

Robert Dietrich

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Tags: Computer science, CUDA, Data acquisition, Heterogeneous systems, MPI, nVidia, OpenACC, OpenCL, Tesla K80, Tesla V100, Thesis

December 1, 2019 by hgpu

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Guoping Long, Jun Yang, Wei Lin

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Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

December 1, 2019 by hgpu

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

November 24, 2019 by hgpu

Compiler-Driven Performance on Heterogeneous Computing Platforms

Artem Chikin

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, Performance, Tesla P100, Tesla V100, Thesis

November 17, 2019 by hgpu

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Aliakbar Panahi, Seyran Saeedi, Tom Arodz

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Tesla V100

November 17, 2019 by hgpu

Performance Evaluation of Advanced Features in CUDA Unified Memory

Steven W. D. Chien, Ivy B. Peng, Stefano Markidis

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1050, Package, Prefetch, Tesla V100

October 27, 2019 by hgpu

MNN: a lightweight deep neural network inference engine

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

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

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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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