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Akid: A Library for Neural Network Research and Production from a Dataism Approach

Shuai Li

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Tags: Computer science, CUDA, Deep learning, Distributed computing, Machine learning, Neural networks, nVidia, Package, Python, TensorFlow

January 8, 2017 by hgpu

Gunrock: GPU Graph Analytics

Yangzihao Wang, Yuechao Pan, Andrew Davidson, Yuduo Wu, Carl Yang, Leyuan Wang, Muhammad Osama, Chenshan Yuan, Weitang Liu, Andy T. Riffel, John D. Owens

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Tags: Computer science, CUDA, Graph theory, nVidia, Package, Tesla K40

January 8, 2017 by hgpu

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Daniel George, E. A. Huerta

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Tags: Algorithms, Artificial intelligence, Astrophysics, CUDA, Deep learning, Filtering, Gravitation, Gravitational waves, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Physics, Signal processing, Tesla K40, Tesla P100

January 4, 2017 by hgpu

Massively Parallel Computation of Accurate Densities for N-body Dark Matter Simulations using the Phase-Space-Element Method

Ralf Kaehler

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Tags: Astrophysics, Computational Physics, CUDA, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Package, Physics, Tesla K80, Tessellation

January 4, 2017 by hgpu

Design and optimization of a portable LQCD Monte Carlo code using OpenACC

Claudio Bonati, Enrico Calore, Simone Coscetti, Massimo D'Elia, Michele Mesiti, Francesco Negro, Sebastiano Fabio Schifano, Giorgio Silvi, Raffaele Tripiccione

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Tags: AMD FirePro W9100, ATI, Computational Physics, Heterogeneous systems, High Energy Physics - Lattice, nVidia, OpenACC, performance portability, Physics, QCD, Tesla K80, Tesla P100

January 4, 2017 by hgpu

Synthesizing Benchmarks for Predictive Modeling

Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Code generation, Compilers, Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, OpenCL, Package

December 31, 2016 by hgpu

Automatic OpenCL Task Adaptation for Heterogeneous Architectures

Pierre Huchant, Marie-Christine Counilh, Denis Barthou

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Tags: Compilers, Computer science, Heterogeneous systems, nVidia, nVidia Quadro K5000, OpenCL, Tesla K20, Tesla M2075

December 31, 2016 by hgpu

Android Malware Classification Using Parallelized Machine Learning Methods

Lifan Xu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, Neural networks, nVidia, OpenCL, OpenMP, Security, Tesla C2050, Thesis

December 31, 2016 by hgpu

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Kaipeng Li, Amanullah Ghazi, Chance Tarver, Jani Boutellier, Mahmoud Abdelaziz, Lauri Anttila, Markku Juntti, Mikko Valkama, Joseph R. Cavallaro

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Tags: ARM, CUDA, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, Signal processing, SoC

December 31, 2016 by hgpu

Language Modeling with Gated Convolutional Networks

Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Performance, Tesla M40, Torch

December 26, 2016 by hgpu

Batched Shift Reduce Parsing with Lists of Vectors on CUDA

Andrew Drozdov

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, nVidia GeForce GTX Titan Black, Package, Torch

December 26, 2016 by hgpu

Function Call Re-Vectorization

Rubens E. A. Moreira, Sylvain Collange, Fernando Magno Quintao Pereira

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Package, String matching

December 26, 2016 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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