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Reducing the Cost of Heuristic Generation with Machine Learning

William F. Ogilvie

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Thesis

July 1, 2018 by hgpu

Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Hugh Leather, Alastair Murray

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Tags: Compilers, Computer science, Deep learning, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 780, OpenCL, Package

July 1, 2018 by hgpu

Introducing Parallelism to the Ranges TS

Gordon Brown, Christopher Di Bella, Michael Haidl, Toomas Remmelg, Ruyman Reyes, Michel Steuwer

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Tags: Computer science, Heterogeneous systems, OpenCL, Package, SYCL

June 28, 2018 by hgpu

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Tim Cramer

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Tags: Computer science, Heterogeneous systems, HPC, Intel Xeon Phi, Memory model, OpenMP, Parallel programming, Thesis

June 28, 2018 by hgpu

Improving tasks throughput on accelerators using OpenCL command concurrency

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

June 28, 2018 by hgpu

Go game move prediction using convolutional neural network

Marek Korenciak

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Tags: Caffe, CNN, Computer science, CUDA, Deep learning, Games, nVidia, nVidia GeForce GTX 1080 Ti, Package, Thesis

June 24, 2018 by hgpu

Synthesis of GPU Programs from High-Level Models

Ziyuan Jiang

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Tags: AMD FirePro S7150, ATI, Computer science, OpenCL, programming model, SoC, Thesis

June 24, 2018 by hgpu

DeepSmith: Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather

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Tags: Compilers, Computer science, Deep learning, nVidia, OpenCL, Package, Tesla K40

June 24, 2018 by hgpu

Energy Efficient Computing on Multi-core Processors: Vectorization and Compression Techniques

Abdullah Al Hasib

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Tags: Compression, Computer science, Data parallelism, Intel Xeon Phi, Prefetch, Programming techniques, Thesis

June 20, 2018 by hgpu

RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

Mohsen Imani, Mohammad Samragh, Yeseong Kim, Saransh Gupta, Farinaz Koushanfar, Tajana Rosing

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Tags: AMD Radeon HD 7970, ATI, CNN, Computer science, Deep learning, FPGA, Hardware Architecture, Neural networks, OpenCL

June 20, 2018 by hgpu

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

Max Danielsson, Hakan Grahn, Thomas Sievert, Jim Rasmusson

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Tags: Computer science, Computer vision, OpenCL

June 20, 2018 by hgpu

Neural Code Comprehension: A Learnable Representation of Code Semantics

Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler

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Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, CUDA, Deep learning, LLVM, LSTM, Machine learning, NLP, nVidia, nVidia GeForce GTX 970, OpenCL, Programming Languages, RNN

June 20, 2018 by hgpu

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

exa-AMD: Exascale Accelerated Materials Discovery

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

No More Shading Languages: Compiling C++ to Vulkan Shaders

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high performance computing on graphics processing units: hgpu.org

Reducing the Cost of Heuristic Generation with Machine Learning

Compiler Fuzzing through Deep Learning

Introducing Parallelism to the Ranges TS

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Improving tasks throughput on accelerators using OpenCL command concurrency

Go game move prediction using convolutional neural network

Synthesis of GPU Programs from High-Level Models

DeepSmith: Compiler Fuzzing through Deep Learning

Energy Efficient Computing on Multi-core Processors: Vectorization and Compression Techniques

RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

Neural Code Comprehension: A Learnable Representation of Code Semantics

Recent source codes

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

exa-AMD: Exascale Accelerated Materials Discovery

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

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