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DeepBach: a Steerable Model for Bach chorales generation

Gaetan Hadjeres, Francois Pachet

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Keras, nVidia, nVidia GeForce GTX 980 Ti, Package

January 23, 2017 by hgpu

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush

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Tags: Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

January 19, 2017 by hgpu

Xeon Phi: A comparison between the newly introduced MIC architecture and a standard CPU through three types of problems

Joakim Kristiansen

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Tags: Algorithms, Computer science, Intel Xeon Phi, Package, Sorting, Thesis

January 19, 2017 by hgpu

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Yasuaki Mitani, Fumihiko Ino, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, String matching

January 19, 2017 by hgpu

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Monica Denham, Karina Laneri, Juan Manuel Morales

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Tags: Biology, CUDA, Earth and Space Sciences, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX Titan, Package, Tesla C2070, Tesla K40

January 16, 2017 by hgpu

An N log N Parallel Fast Direct Solver for Kernel Matrices

Chenhan D. Yu, William B. March, George Biros

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Tags: Algorithms, Computational Complexity, Computer science, Factorization, Intel Xeon Phi, Machine learning, Package, Performance

January 16, 2017 by hgpu

Decoding with Finite-State Transducers on GPUs

Arturo Argueta, David Chiang

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Tags: Algorithms, Computer science, CUDA, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Speech recognition, Tesla K40, Tesla P100

January 16, 2017 by hgpu

Software Prefetching for Indirect Memory Accesses

Sam Ainsworth, Timothy M. Jones

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Tags: ARM, Benchmarking, Compilers, Computer science, Intel Xeon Phi, LLVM, MPI, Package, Prefetch

January 10, 2017 by hgpu

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Tian Zhao, Xiaobing Huang, Yu Cao

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Tags: Computer science, CUDA, Deep learning, DSL, Java, Machine learning, nVidia, Package, Programming Languages, Scala, Tesla K40

January 10, 2017 by hgpu

A Framework for Dense Triangular Matrix Kernels on Various Manycore Architectures

Ali Charara, David Keyes, Hatem Ltaief

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Tags: BLAS, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, Matrix multiplication, nVidia, Package, Tesla K40

January 8, 2017 by hgpu

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

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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