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Porting tree-based hash table compression to GPGPU model checking

Danny Bergsma

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Tags: Algorithms, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX Titan XP, Package, Thesis

December 29, 2019 by hgpu

Automatic Performance Optimisation of Parallel Programs for GPUs via Rewrite Rules

Toomas Remmelg

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Tags: AMD Radeon R9 295X2, ARM, ATI, Computer science, nVidia, nVidia GeForce GTX Titan Black, OpenCL, Performance, performance portability, Thesis

December 29, 2019 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

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Jocelyn Sunseri, David Ryan Koes

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Tags: Chemistry, Computer science, CUDA, Deep learning, Machine learning, Molecular modeling, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Package, PyTorch, TensorFlow

December 15, 2019 by hgpu

Array Languages Make Neural Networks Fast

Artjoms Šinkarovs, Hans-Nikolai Vießmann, Sven-Bodo Scholz

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package, Python, PyTorch, TensorFlow, Tesla K20

December 15, 2019 by hgpu

The BondMachine toolkit: Enabling Machine Learning on FPGA

Mirko Mariotti, Loriano Storchi, Daniele Spiga, Davide Salomoni, Tommaso Boccali, Daniele Bonacorsi

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Tags: Computer science, FPGA, Go, Machine learning

December 8, 2019 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia Quadro GP100, Package, Python, PyTorch

December 8, 2019 by hgpu

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez, Rahul Khanna

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Tags: Computer science, OpenCL, Package

December 8, 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

FBLAS: Streaming Linear Algebra Kernels on FPGA

Tiziano De Matteis, Johannes de Fine Licht, Torsten Hoefler

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Tags: BLAS, Computer science, FPGA, Linear Algebra, OpenCL, Package

December 1, 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

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini

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Tags: Algorithms, Computer science, IoT, Machine learning, Neural networks, Package

December 1, 2019 by hgpu

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

CLUEstering: a high-performance density-based clustering library for scientific computing

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

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