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Found 5 papers, 0 papers with code
Evaluating Retrieval for Multi-domain Scientific Publications
This paper provides an overview of the xDD/LAPPS Grid framework and provides results of evaluating the AskMe retrievalengine using the BEIR benchmark datasets.
Performance portability through machine learning guided kernel selection in SYCL libraries
Automatically tuning parallel compute kernels allows libraries and frameworks to achieve performance on a wide range of hardware, however these techniques are typically focused on finding optimal kernel parameters for particular input sizes and parameters.
Towards automated kernel selection in machine learning systems: A SYCL case study
Automated tuning of compute kernels is a popular area of research, mainly focused on finding optimal kernel parameters for a problem with fixed input sizes.
Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels
Over recent years heterogeneous systems have become more prevalent across HPC systems, with over 100 supercomputers in the TOP500 incorporating GPUs or other accelerators.
Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN
There are a number of approaches available to developers for utilizing GPGPU technologies such as SYCL, OpenCL and CUDA, however many applications require the same low level mathematical routines.
