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TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Compilers, Computer science, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX Titan X, Optimization, Package, performance portability

October 13, 2018 by hgpu

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Pierre Huchant, Denis Barthou, Marie-Christine Counilh

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Tags: Computer science, Heterogeneous systems, load balancing, nVidia, OpenCL, Tesla M2075

October 13, 2018 by hgpu

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

David W. Brown

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 750 Ti, Package

October 6, 2018 by hgpu

Exascale Deep Learning for Climate Analytics

Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston

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Tags: CUDA, Deep learning, Earth and Space Sciences, Neural networks, nVidia, TensorFlow, Tesla V100, Weather prediction

October 6, 2018 by hgpu

HSTREAM: A directive-based language extension for heterogeneous stream computing

Suejb Memeti, Sabri Pllana

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro P 4000, OpenMP

October 6, 2018 by hgpu

On Reinforcement Learning for Full-length Game of StarCraft

Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu

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Tags: AI, Algorithms, Artificial intelligence, Computer science, Deep learning, Games, Machine learning, nVidia, Tesla K40

October 6, 2018 by hgpu

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Swen Boehm, Swaroop Pophale, Veronica G. Vergara Larrea, Oscar Hernandez

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenACC, OpenCL, OpenMP, performance portability, Tesla K20, Tesla V100

September 23, 2018 by hgpu

Parallel LZ77 Decoding using a GPU

Emmanuel Morfiadakis

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 840 M, Thesis

September 23, 2018 by hgpu

Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners

Chang Sun

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Tags: Computer science, Machine learning, Neural networks, nVidia, OpenCL, Tesla P100, Thesis

September 23, 2018 by hgpu

Support for Parallel Scan in OpenMP

Eder Maicol Gomez Zegarra

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Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, OpenCL, OpenMP, Sorting, Thesis

September 23, 2018 by hgpu

SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs

Matthias Springer

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Programming Languages

September 23, 2018 by hgpu

A deep learning approach to autonomous lunar landing

Ilaria Bloise, Marcello Orlandelli

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, LSTM, Machine learning, Neural networks, nVidia, RNN, Tesla K20, Thesis

September 16, 2018 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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