Optimising AI Training Deployments using Graph Compilers and Containers
| Autor: | Alfio Lazzaro, Nina Mujkanovic, Karthee Sivalingam |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Science - Artificial Intelligence Cloud computing 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Software 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences Computer Science - Performance Artificial neural network business.industry Deep learning Virtualization Supercomputer Performance (cs.PF) Artificial Intelligence (cs.AI) Computer architecture Computer Science - Distributed Parallel and Cluster Computing Graph (abstract data type) 020201 artificial intelligence & image processing Distributed Parallel and Cluster Computing (cs.DC) Compiler Artificial intelligence business computer |
| Zdroj: | HPEC 2020 IEEE High Performance Extreme Computing Conference (HPEC) |
| Popis: | Artificial Intelligence (AI) applications based on Deep Neural Networks (DNN) or Deep Learning (DL) have become popular due to their success in solving problems likeimage analysis and speech recognition. Training a DNN is computationally intensive and High Performance Computing(HPC) has been a key driver in AI growth. Virtualisation and container technology have led to the convergence of cloud and HPC infrastructure. These infrastructures with diverse hardware increase the complexity of deploying and optimising AI training workloads. AI training deployments in HPC or cloud can be optimised with target-specific libraries, graph compilers, andby improving data movement or IO. Graph compilers aim to optimise the execution of a DNN graph by generating an optimised code for a target hardware/backend. As part of SODALITE (a Horizon 2020 project), MODAK tool is developed to optimise application deployment in software defined infrastructures. Using input from the data scientist and performance modelling, MODAK maps optimal application parameters to a target infrastructure and builds an optimised container. In this paper, we introduce MODAK and review container technologies and graph compilers for AI. We illustrate optimisation of AI training deployments using graph compilers and Singularity containers. Evaluation using MNIST-CNN and ResNet50 training workloads shows that custom built optimised containers outperform the official images from DockerHub. We also found that the performance of graph compilers depends on the target hardware and the complexity of the neural network. HPEC IEEE, 6 pages, 5 figues, 1 table |
| Databáze: | OpenAIRE |
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