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Integer sorting on multicores: some (experiments and) observations

Alexandros V. Gerbessiotis

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Performance, Sorting, Tesla K40

September 3, 2017 by hgpu

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

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Tags: Computer science, CUDA, Deep learning, Image recognition, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Performance, TensorFlow, Tesla K40

September 3, 2017 by hgpu

GPU Array Access Auto-Tuning

Nicolas Weber

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GT 620, nVidia GeForce GT 730, nVidia GeForce GTX 1080, nVidia GeForce GTX 480, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 570, nVidia GeForce GTX 590, nVidia GeForce GTX 680, nVidia GeForce GTX 780, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Performance, performance portability, Tesla C2070, Tesla K20, Thesis

August 8, 2017 by hgpu

AutOMP: An Automatic OpenMP Parallelization Generator for Variable-Oriented High-Performance Scientific Codes

Gal Oren, Yehuda Ganan, Guy Malamud

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Tags: Code generation, Computer science, Intel Xeon Phi, OpenMP, Performance

August 1, 2017 by hgpu

Directive-Based Partitioning and Pipelining for Graphical Processing Units

X. Cui, T. R. W. Scogland, B. R. de Supinski, W. Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, OpenACC, OpenCL, OpenMP, Performance, Task scheduling, Tesla K40

August 1, 2017 by hgpu

On Simplifying and Optimizing Programs for Heterogeneous Computing Systems

Ivan Grasso

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Tags: AMD FirePro S9000, ATI, ATI Radeon HD 5870, Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Tesla K20, Thesis

July 25, 2017 by hgpu

Parallelization of an Unsteady ALE Solver with Deforming Mesh Using OpenACC

Wenpeng Ma, Zhonghua Lu, Wu Yuan, Xiaodong Hu

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Tags: Algorithms, Computer science, nVidia, OpenACC, OpenMP, Performance, Tesla K20, Triangular meshes

July 22, 2017 by hgpu

Automatically Selecting Profitable Thread Block Sizes Using Machine Learning

Tiffany A. Connors

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Tags: Computer science, CUDA, Machine learning, nVidia, Performance, Tesla K40, Thesis

July 22, 2017 by hgpu

Cooperative Kernels: GPU Multitasking for Blocking Algorithms

Tyler Sorensen, Hugues Evrard, Alastair F. Donaldson

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Tags: Algorithms, Computer science, OpenCL, Performance, Programming Languages

July 14, 2017 by hgpu

Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor

Chansup Byun, Jeremy Kepner, William Arcand, David Bestor, Bill Bergeron, Vijay Gadepally, Michael Houle, Matthew Hubbell, Michael Jones, Anna Klein, Peter Michaleas, Lauren Milechin, Julie Mullen, Andrew Prout, Antonio Rosa, Siddharth Samsi, Charles Yee, Albert Reuther

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Tags: Benchmarking, Caffe, Computer science, Deep learning, Intel Xeon Phi, Machine learning, Performance

July 14, 2017 by hgpu

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

Jiazhen Gu, Huan Liu, Yangfan Zhou, Xin Wang

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Suffix trees

July 14, 2017 by hgpu

A Similarity Measure for GPU Kernel Subgraph Matching

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K80, Tesla M40, Tesla P100

July 14, 2017 by hgpu

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

PortLBM: A Portable Lattice Boltzmann Tool Leveraging SYCL on AMD, NVIDIA, and Intel GPUs

Harness Engineering for LLM-Driven GPU Kernel Generation

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

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

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

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

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