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A readahead prefetcher for GPU file system layer

Vasilis Dimitsas, Mark Silberstein

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Tags: Benchmarking, Computer science, CUDA, nVidia, Prefetch, Tesla K40

September 19, 2021 by hgpu

Measurement and Analysis of GPU-accelerated Applications with HPCToolkit

Keren Zhou, Laksono Adhianto, Jonathon Anderson, Aaron Cherian, Dejan Grubisic, Mark Krentel, Yumeng Liu, Xiaozhu Meng, John Mellor-Crummey

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Tags: AMD Radeon Instinct Mi50, ATI, Code generation, Computer science, HPC, nVidia, OpenCL, Performance, Tesla V100

September 19, 2021 by hgpu

GPU Algorithms for Efficient Exascale Discretizations

Ahmad Abdelfattah, Valeria Barra, Natalie Beams, Ryan Bleile, Jed Brown, Jean-Sylvain Camier, Robert Carson, Noel Chalmers, Veselin Dobrev, Yohann Dudouit, Paul Fischer, Ali Karakus, Stefan Kerkemeier, Tzanio Kolev, Yu-Hsiang Lan, Elia Merzari, Misun Min, Malachi Phillips, Thilina Rathnayake, Robert Rieben, Thomas Stitt, Ananias Tomboulides, Stanimire Tomov, Vladimir Tomov, Arturo Vargas, Tim Warburton, Kenneth Weiss

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Tags: Algorithms, AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Finite element method, nVidia, OCCA, Tesla V100

September 19, 2021 by hgpu

A Study of Mixed Precision Strategies for GMRES on GPUs

Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki, Erik G. Boman, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, performance portability, Tesla V100

September 19, 2021 by hgpu

Supporting CUDA for an extended RISC-V GPU architecture

Ruobing Han, Blaise Tine, Jaewon Lee, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, LLVM, nVidia, OpenCL, Package, Programming Languages

September 5, 2021 by hgpu

Data-Oriented Language Implementation of Lattice-Boltzmann Method for Dense and Sparse Geometries

Tadeusz Tomczak

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Tags: cfd, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX Titan XP, Package

September 5, 2021 by hgpu

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

Tian Lan, Sunil Srinivasa, Stephan Zheng

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Python, Tesal V100

September 5, 2021 by hgpu

LocalityGuru: A PTX Analyzer for Extracting Thread Block-level Locality in GPGPUs

Devashree Tripathy, AmirAli Abdolrashidi, Quan Fan, Daniel Wong, Manoranjan Satpathy

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Tags: Computer science, CUDA, nVidia, PTX

September 5, 2021 by hgpu

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

Navdeep Katel, Vivek Khandelwal, Uday Bondhugula

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Tags: Code generation, Computer science, CUBLAS, CUDA, HPC, Matrix multiplication, nVidia, nVidia GeForce RTX 3090, Package

September 5, 2021 by hgpu

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

Ludwig Kratzl

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Intel, Intel UHD 630, MPI, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, performance portability, SYCL, Thesis

August 29, 2021 by hgpu

The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product

Rubao Lee, Minghong Zhou, Chi Li, Shenggang Hu, Jianping Teng, Dongyang Li, Xiaodong Zhang

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia Quadro RTX 8000

August 29, 2021 by hgpu

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity

Wei Niu, Zhengang Li, Xiaolong Ma, Peiyan Dong, Gang Zhou, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren

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Tags: Artificial intelligence, Code generation, Computer science, Deep learning, DSL, Neural networks, nVidia, nVidia Titan RTX, OpenCL

August 29, 2021 by hgpu

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

INT v.s. FP: A framework to compare low-bit integer and float-point formats

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

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

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

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

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

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

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

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