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hgpu.org » Programming

Characterizing Optimizations to Memory Access Patterns using Architecture-Independent Program Features

Aditya Chilukuri, Josh Milthorpe, Beau Johnston

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Tags: Computer science, Heterogeneous systems, Memory, nVidia, OpenCL, Performance, Tesla P100

March 29, 2020 by hgpu

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

Nicolas Weber, Felipe Huici

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V100, nVidia Quadro P 4000, PyTorch

March 29, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels, Rastislav Bodik, Vinod Grover

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce RTX 2080 Ti, nVidia Quadro GV100, Programming Languages

March 22, 2020 by hgpu

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Peng Zhang, Jianbin Fang, Canqun Yang, Chun Huang, Tao Tang, Zheng Wang

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Tags: Analytical model, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, OpenMP, Package

March 22, 2020 by hgpu

Learnergy: Energy-based Machine Learners

Mateus Roder, Gustavo Henrique de Rosa, João Paulo Papa

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, Machine learning, Neural networks, nVidia, Package, Python, PyTorch

March 22, 2020 by hgpu

Abstracting OpenCL for Multi-Application Workloads on CPU-FPGA Clusters

Graham Mark Holland

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Tags: ARM, Cloud, Computer science, FPGA, Heterogeneous systems, OpenCL, Task scheduling, Thesis

March 15, 2020 by hgpu

Data Movement Optimization for High-Performance Computing

Tobias Gysi

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Tags: Computer science, CUDA, HPC, nVidia, Tesla K20, Thesis

March 15, 2020 by hgpu

Automated test generation for OpenCL kernels using fuzzing and constraint solving

Chao Peng, Ajitha Rajan

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Tags: Benchmarking, Computer science, OpenCL, Package

March 15, 2020 by hgpu

Towards Green Computing: A Survey of Performance and Energy Efficiency of Different Platforms using OpenCL

Philip Heinisch, Katharina Ostaszewski, Hendrik Ranocha

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Tags: AMD FirePro S9150, AMD Radeon R9 280X, ARM, ATI, Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX 570, nVidia GRID K520, nVidia Titan RTX, OpenCL, survey, Tesla C2075, Tesla P100, Tesla V100

March 15, 2020 by hgpu

Performance and energy footprint assessment of FPGAs and GPUs on HPC systems using Astrophysics application

David Goz, Georgios Ieronymakis, Vassilis Papaefstathiou, Nikolaos Dimou, Sara Bertocco, Giuliano Taffoni, Francesco Simula, Antonio Ragagnin, Luca Tornatore, Igor Coretti

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Tags: ARM, Astrophysics, CUDA, FPGA, Heterogeneous systems, HPC, HSL, N-body simulation, nVidia, OpenCL, Performance, Physics, SoC, Tesla V100

March 15, 2020 by hgpu

Solving convex optimization problems on FPGA using OpenCL

Martijn Berkers

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Tags: Algorithms, Computer science, Design space exploration, FPGA, OpenCL, Thesis

March 8, 2020 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

Data-efficient LLM Fine-tuning for Code Generation

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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