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Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Jose Marques, Gabriel Falcao, Luis A. Alexandre

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Tags: ATI, ATI Radeon HD 7500 M, CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Matlab, Neural networks, nVidia, nVidia GeForce GTX 840 M, nVidia GeForce GTX 940 M, nVidia GeForce GTX 950 M, OpenCL, Tesla K20

December 10, 2017 by hgpu

Beehive SPIR-V Toolkit

Beehive SPIR-V Toolkit: A Composable and Functional API for Runtime SPIR-V Code Generation

CATO: automatic source transformation to apply HPC frameworks with minimal user interaction

Compiler-assisted distribution of OpenMP code for improved scalability

ADIOS2.jl: a Julia interface to ADIOS2

Julia as a unifying end-to-end workflow language on the Frontier exascale system

Code generated HPC Kernels Using Llama-2-70b

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

Scope is all you need: Transforming LLMs for HPC Code

Scope is all you need: Transforming LLMs for HPC Code

pocl - Portable Computing Language

PoCL-R: An Open Standard Based Offloading Layer for Heterogeneous Multi-Access Edge Computing with Server Side Scalability

HPAC-Offload: Accelerating HPC Applications with Portable Approximate Computing on the GPU

HPAC-Offload: Accelerating HPC Applications with Portable Approximate Computing on the GPU

openCLDispatcher

Novel insights on atomic synchronization for sort-based group-by on GPUs

CEED Benchmarks

Performant low-order matrix-free finite element kernels on GPU architectures

Sieve: Stratified GPU-Compute Workload Sampling

Sieve: Stratified GPU-Compute Workload Sampling

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