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Implementing Efficient, Portable Computations for Machine Learning

Matthew Walter Moskewicz

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Tags: Algorithms, AMD R9 Nano, ATI, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Thesis

May 24, 2017 by hgpu

Intel Xeon Phi acceleration of Hybrid Total FETI solver

Michal Merta, Lubomir Riha, Ondrej Meca, Alexandros Markopoulos, Tomas Brzobohaty, Tomas Kozubek, Vit Vondrak

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Tags: Algorithms, Computer science, Intel Xeon Phi, Laplace and Poisson equation, OpenMP, Package, Sparse direct solvers, Sparse matrix

May 24, 2017 by hgpu

Parallel and in-process compilation of individuals for genetic programming on GPU

Hakan Ayral, Songul Albayrak

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GRID K520, Operating systems, Package

May 24, 2017 by hgpu

MEDINA: MECCA Development in Accelerators – KPP Fortran to CUDA source-to-source Preprocessor

Michail Alvanos, Theodoros Christoudias

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Tags: Chemistry, Compilers, Computer science, CUDA, Fortran, nVidia, Package, Python

May 22, 2017 by hgpu

Inferring the Scheduling Policies of an Embedded CUDA GPU

Nathan Otterness, Ming Yang, Tanya Amert, James H. Anderson, F. Donelson Smith

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Tegra TX1, Package, Task scheduling

May 22, 2017 by hgpu

CLBlast: A Tuned OpenCL BLAS Library

Cedric Nugteren

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Tags: AMD Radeon R9 M370X, ARM, ATI, BLAS, Computer science, Intel HD 5100, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan X, OpenCL, Package

May 18, 2017 by hgpu

Efficient Parallel Methods for Deep Reinforcement Learning

Alfredo V. Clemente, Humberto N. Castejon, Arjun Chandra

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Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GXT 980 Ti, Package, Python, TensorFlow

May 18, 2017 by hgpu

Block-Parallel IDA* for GPUs

Satoru Horie, Alex Fukunaga

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GRID K520, Package

May 11, 2017 by hgpu

Accelerating solutions of PDEs with GPU-based swept time-space decomposition

Daniel J Magee, Kyle E Niemeyer

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Tags: Algorithms, cfd, Computational Physics, CUDA, Differential equations, Fluid dynamics, nVidia, Package, Partial differential equations, PDEs, Physics, Tesla K40

May 11, 2017 by hgpu

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

Weihua Guo, You Xu, Xueyang Feng

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Tags: Biology, Computer science, Deep learning, Medicine, nVidia, nVidia GeForce GTX 1080, Package, Python, TensorFlow

May 11, 2017 by hgpu

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Konstantinos Krommydas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Performance, Tesla K20, Thesis

May 9, 2017 by hgpu

Efficient Parallel Strategy Improvement for Parity Games

John Fearnley

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Logic in Computer Science, nVidia, nVidia GeForce GTX 780, Package

May 9, 2017 by hgpu

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

See all packages

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high performance computing on graphics processing units: hgpu.org

Implementing Efficient, Portable Computations for Machine Learning

Intel Xeon Phi acceleration of Hybrid Total FETI solver

Parallel and in-process compilation of individuals for genetic programming on GPU

MEDINA: MECCA Development in Accelerators – KPP Fortran to CUDA source-to-source Preprocessor

Inferring the Scheduling Policies of an Embedded CUDA GPU

CLBlast: A Tuned OpenCL BLAS Library

Efficient Parallel Methods for Deep Reinforcement Learning

Block-Parallel IDA* for GPUs

Accelerating solutions of PDEs with GPU-based swept time-space decomposition

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Efficient Parallel Strategy Improvement for Parity Games

Recent source codes

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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