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Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Beau Johnston

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Tags: Algorithms, AMD Radeon RX 480, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K40, Thesis

September 15, 2019 by hgpu

PySPH: a Python-based framework for smoothed particle hydrodynamics

Prabhu Ramachandran, Kunal Puri, Aditya Bhosale, A Dinesh, Abhinav Muta, Pawan Negi, Rahul Govind, Suraj Sanka, Pankaj Pandey, Chandrashekhar Kaushik, Anshuman Kumar, Ananyo Sen, Rohan Kaushik, Mrinalgouda Patil, Deep Tavker, Dileep Menon, Vikas Kurapati, Amal S Sebastian, Arkopal Dutt, Arpit Agarwal

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Tags: cfd, Computational Physics, CUDA, Fluid dynamics, Mathematical Software, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

September 15, 2019 by hgpu

Efficient Interleaved Batch Matrix Solvers for CUDA

Andrew Gloster, Enda Carroll, Miguel Bustamante, Lennon O'Naraigh

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Tags: Algorithms, Computational Physics, Computer science, CUDA, Matrix inversion, nVidia, Package, Tesla V100

September 15, 2019 by hgpu

ArborX: A Performance Portable Search Library

D. Lebrun-Grandie, A. Prokopenko, B. Turcksin, S. R. Slattery

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, MPI, OpenMP, Package, performance portability

September 8, 2019 by hgpu

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, Eduard Hovy

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package

September 8, 2019 by hgpu

Compositional Deep Learning in Futhark

Duc Minh Tran, Troels Henriksen, Martin Elsman

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 2080 Ti, Package, TensorFlow

September 1, 2019 by hgpu

Demystifying the MLPerf Benchmark Suite

Snehil Verma, Qinzhe Wu, Bagus Hanindhito, Gunjan Jha, Eugene B. John, Ramesh Radhakrishnan, Lizy K. John

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100, Tesla V100

September 1, 2019 by hgpu

stdgpu: Efficient STL-like Data Structures on the GPU

Patrick Stotko

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, Package

August 25, 2019 by hgpu

Memory-Efficient Object-Oriented Programming on GPUs

Matthias Springer

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 940 M, nVidia GeForce GTX 980, nVidia GeForce GTX Titan XP, Package, Programming Languages, Thesis

August 25, 2019 by hgpu

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

Muhammad K.A. Hamdan, Diane Rover, John Just

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Tags: Computer science, Deep learning, Machine learning, Neural networks, Package

August 18, 2019 by hgpu

High Performance Computing via High Level Synthesis

Mehdi Roozmeh

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, Nearest neighbour, nVidia, OpenCL, Package, Signal processing, Thesis

August 18, 2019 by hgpu

SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

Eleftherios Avramidis, Marta Lalik, Ozgur E. Akman

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Tags: AMD FirePro W8100, ATI, Computer science, Differential equations, Mathematical Software, nVidia, OpenCL, Package, Tesla V100

August 18, 2019 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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