Machine learning and big scientific data
This paper reviews some of the challenges posed by the huge growth of experimental data
generated by the new generation of large-scale experiments at UK national facilities at the …
generated by the new generation of large-scale experiments at UK national facilities at the …
Distributed training of deep learning models: A taxonomic perspective
Distributed deep learning systems (DDLS) train deep neural network models by utilizing the
distributed resources of a cluster. Developers of DDLS are required to make many decisions …
distributed resources of a cluster. Developers of DDLS are required to make many decisions …
Mlperf mobile inference benchmark: An industry-standard open-source machine learning benchmark for on-device ai
This paper presents the first industry-standard open-source machine learning (ML)
benchmark to allow performance and accuracy evaluation of mobile devices with different AI …
benchmark to allow performance and accuracy evaluation of mobile devices with different AI …
Edge AIBench: towards comprehensive end-to-end edge computing benchmarking
In edge computing scenarios, the distribution of data and collaboration of workloads on
different layers are serious concerns for performance, privacy, and security issues. So for …
different layers are serious concerns for performance, privacy, and security issues. So for …
AIoT bench: towards comprehensive benchmarking mobile and embedded device intelligence
Due to increasing amounts of data and compute resources, the deep learning achieves
many successes in various domains. Recently, researchers and engineers make effort to …
many successes in various domains. Recently, researchers and engineers make effort to …
Benchmarking the performance and energy efficiency of AI accelerators for AI training
Deep learning has become widely used in complex AI applications. Yet, training a deep
neural network (DNNs) model requires a considerable amount of calculations, long running …
neural network (DNNs) model requires a considerable amount of calculations, long running …
Gnnmark: A benchmark suite to characterize graph neural network training on gpus
Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning
algorithms to train on non-euclidean data. GNNs are widely used in recommender systems …
algorithms to train on non-euclidean data. GNNs are widely used in recommender systems …
[HTML][HTML] A BenchCouncil view on benchmarking emerging and future computing
J Zhan - BenchCouncil Transactions on Benchmarks, Standards …, 2022 - Elsevier
The measurable properties of the artifacts or objects in the computer, management, or
finance disciplines are extrinsic, not inherent—dependent on their problem definitions and …
finance disciplines are extrinsic, not inherent—dependent on their problem definitions and …
AIBench training: Balanced industry-standard AI training benchmarking
Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks.
Only using a few AI component benchmarks like MLPerf alone in the other stages may lead …
Only using a few AI component benchmarks like MLPerf alone in the other stages may lead …
HPC AI500: a benchmark suite for HPC AI systems
In recent years, with the trend of applying deep learning (DL) in high performance scientific
computing, the unique characteristics of emerging DL workloads in HPC raise great …
computing, the unique characteristics of emerging DL workloads in HPC raise great …