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Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Taghreed Bagies, Wei Le, Jeremy Sheafer, Ali Jannesari

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance, Tesla V100

June 18, 2023 by hgpu

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Gargi Alavani, Jineet Desai, Snehanshu Saha, Santonu Sarkar

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, Package, Tesla K20, Tesla M60, Tesla V100

June 11, 2023 by hgpu

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Zheming Jin, Jeffrey S. Vetter

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Tags: Benchmarking, Bioinformatics, Computer science, CUDA, Heterogeneous systems, nVidia, Package, performance portability, SYCL, Tesla V100

April 16, 2023 by hgpu

Energy-Efficient GPU Clusters Scheduling for Deep Learning

Diandian Gu, Xintong Xie, Gang Huang, Xin Jin, Xuanzhe Liu

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Tags: Computer science, Deep learning, Energy-efficient computing, GPU cluster, Neural networks, nVidia, Tesla V100

April 16, 2023 by hgpu

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary Hall

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Tags: Bayesian, Computer science, Energy-efficient computing, MPI, nVidia, OpenMP, Package, Tesla V100

April 2, 2023 by hgpu

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

Giorgis Georgakoudis, Konstantinos Parasyris, Chunhua Liao, David Beckingsale, Todd Gamblin, Bronis de Supinski

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, OpenMP, performance portability, Tesla P100, Tesla V100

March 19, 2023 by hgpu

Runtime Support for Performance Portability on Heterogeneous Distributed Platforms

Polykarpos Thomadakis, Nikos Chrisochoides

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, performance portability, Tesla V100

March 12, 2023 by hgpu

EvoTorch: Scalable Evolutionary Computation in Python

Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka, Paweł Liskowski, Rupesh Kumar Srivastava

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Tags: Artificial intelligence, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, Package, Python, PyTorch, Tesla V100

March 5, 2023 by hgpu

EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Manuel de Castro, Inmaculada Santamaria-Valenzuela, Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: AMD FirePro W9100, ATI, Computer science, Heterogeneous systems, Numerical simulation, nVidia, OpenCL, Package, Stencil computation, Tesla V100

February 12, 2023 by hgpu

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

Kazuaki Matsumura, Simon Garcia De Gonzalo, Antonio J. Peña

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenACC, Package, PTX, Tesla K40, Tesla K80, Tesla V100

February 5, 2023 by hgpu

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Pablo F. Zubieta Rico, Ludwig Schneider, Gustavo Perez-Lemus, Riccardo Alessandri, Siva Dasetty, Cintia A. Menéndez, Yiheng Wu, Yezhi Jin, Trung Nguyen, John Parker, Andrew L. Ferguson, Juan J. de Pablo

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Tags: Biology, Chemistry, Computational Physics, CUDA, Molecular dynamics, nVidia, Package, Physics, Python, Tesla V100

January 22, 2023 by hgpu

A Programming Model for GPU Load Balancing

Muhammad Osama, Serban D. Porumbescu, John D. Owens

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Tags: Algorithms, Computer science, CUDA, load balancing, nVidia, Package, Tesla V100

January 15, 2023 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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