A Data Center Design for Scalable Parallel workloads
This research examines the Graphcore data center architecture that enables highly scalable parallel processing for Artificial Intelligence (AI) and High-Performance Computing (HPC). This architecture encompasses efficient low-latency communications between Intelligence Processing Units (IPUs) within a node, within a rack, and across a data center with hundreds or even thousands of accelerators to handle exponentially increasing AI model complexity. The IPU fabric dynamically connects IPU accelerators with disaggregated servers and storage. Critically, this agile platform for parallel applications supports a comprehensive software stack to develop and optimize these workloads using open-source frameworks and Graphcore-developed libraries and development tools. Shortly, we look forward to seeing high-scale benchmarks to validate this highly scalable platform’s potential.
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