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Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs

Endri Taka, Dimitrios Gourounas, Andreas Gerstlauer, Diana Marculescu, Aman Arora

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Tags: AI, Computer science, Deep learning, FPGA, GEMM, Matrix multiplication

April 21, 2024 by hgpu

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Francesco Lumpp

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance, Thesis

April 21, 2024 by hgpu

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

Peter Thoman, Fabian Knorr, Luigi Crisci

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Tags: Computer science, HPC, Package, Performance, SYCL

April 21, 2024 by hgpu

Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

Feng Liang, Zhen Zhang, Haifeng Lu, Victor C. M. Leung, Yanyi Guo, Xiping Hu

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia A100, survey, Task scheduling

April 21, 2024 by hgpu

A Systematic Literature Survey of Sparse Matrix-Vector Multiplication

Jianhua Gao, Bingjie Liu, Weixing Ji, Hua Huang

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Tags: Computer science, FPGA, Heterogeneous systems, Linear Algebra, Machine learning, Overview, Sparse matrix

April 14, 2024 by hgpu

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

Peter Thoman, Philip Salzmann

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Tags: Benchmarking, Computer science, GPU cluster, HPC, nVidia, nVidia V100, Package, SYCL

April 14, 2024 by hgpu

High Performance Privacy Preserving AI

Jayavanth Shenoy, Patrick Grinaway, Shriphani Palakodety

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Tags: AI, Artificial intelligence, Book, Computer science, Neural networks, Security

April 14, 2024 by hgpu

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

Keller Jordan

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Tags: Computer science, Computer vision, Image processing, Machine learning, nVidia

April 7, 2024 by hgpu

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Erik D. Huckvale, Hunter N.B. Moseley

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Tags: Computer science, CUDA, nVidia, Package, Performance, Profiling, Python

April 7, 2024 by hgpu

Seer: Predictive Runtime Kernel Selection for Irregular Problems

Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Linear Algebra, load balancing, Performance, Sparse matrix

April 7, 2024 by hgpu

Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

Wentao Liang, Norihisa Fujita, Ryohei Kobayashi, Taisuke Boku

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, oneAPI, OpenCL, SYCL, Tesla V100

April 7, 2024 by hgpu

Full-Scale File System Acceleration on GPU

Peter Maucher, Lennard Kittner, Nico Rath, Gregor Lucka, Lukas Werling, Yussuf Khalil, Thorsten Gröninger, Frank Bellosa

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Tags: AMD Radeon RX 6800, ATI, Computer science, Data Structures and Algorithms, HIP, Performance

March 24, 2024 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

A Systematic Literature Survey of Sparse Matrix-Vector Multiplication

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

High Performance Privacy Preserving AI

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Seer: Predictive Runtime Kernel Selection for Irregular Problems

Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

Full-Scale File System Acceleration on GPU

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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