Supercomputing.life
The Supercomputing.life platform is a network of General Purpose Compute Nodes designed by the Distributed Computing Research Group at Balanced City ¹. Compute Node Architecture · Overview A Compute Node is a group of computers designed to work together on large projects. Distributed computing research and experience in performance engineering guided the design of the General Purpose Compute Node, GPCN2021. General Purpose Compute Node 2021 Compute Nodes have 15 individual RPi computers. · 12 x RPi A+ perform primary computation, the scientific work that the system is designed to accomplish, using their 48 CPUs. · 3 x RPi B orchestrate overall workflow, data and communication, using their 12 CPUs. raspberrypi.org 12 x Raspberry Pi Model A+ 3 x Raspberry Pi Model B 64bit ARM Cortex-53 quad-core 1.4GHz 64bit ARM Cortex-A72 quad-core 1.5GHz Each individual RPi operates a quad-core processor with 4 Central Processing Units (CPUs). A General Purpose Compute Node has 60 CPUs. Each CPU is capable of performing an independent computational task. Compute Node Workflow · Primary Computation RPi B managers divide the workload into partitions, preparing data and computational tasks for up to 48 CPUs on the 12 RPi A+ primary computers. Primary computers deliver processed data back to managers upon completion. Managers consolidate processed data, checking for accuracy and consistency. Lastly, managers send processed data from the Compute Node to the project's final inventory. Compute Node Software · Scientific Libraries, Methods and Tools Scientific libraries, methods and tools used on this architecture are created de novo with inspiration from leadership initiatives such as the NIH Biowulf Cluster ² and Department of Energy National Laboratories ³. · Supercomputing.life Applications Supercomputing.life applications support life science research and are developed with databases provided by the National Center for Biotechnology Information, a division of the National Library of Medicine at the National Institutes of Health in the United States. These applications are collectively referred to as the Learning Health Systems and are accessible through the public interface at OpenMD.life This machine teaching platform benefits from natural intelligence of users around the world. Operational Performance · Initial Deployment Compute Nodes began scientific operation in Q2 2019 performing CPU intensive language processing for the Learning Health Systems. · Utilitization The utilization profile of a Compute Node typically has 20 to 35 primary compute CPUs operating in parallel continuously for several hours up to several days. Technical measures of processor clock speed are less relevant than the number of independent processing tasks performed simultaneously. · Developer Productivity Engineered to maximize performance of developers through automation and ease of use, Compute Node architecture eliminates productivity barriers for rapid prototyping, programming, optimizing and deploying supercomputing systems. Easily deploying 1,000 computational tasks with no complexity burden is a practical measure of system success from the perspective of a developer. · Operations per second Computing benchmarks measure floating-point operations per second (FLOPS). The Compute Nodes described here are capable of... · 64 GigaFLOPS peak CPU primary computation · 85 GigaFLOPS peak CPU total computation Sustained operation is kept below 75% peak capacity, approximately 50 GigaFLOPS of primary computation. · Scalability An indefinite number of Compute Nodes can be used to accomplish computational goals. This scalability is a motivating design requirement of the Compute Node architecture. · Large Scale · 1012 FLOPS 25 Compute Nodes are estimated to sustain 1 TeraFLOP running on 1,000 CPUs. · Extreme Scale · 1015 FLOPS 25,000 Compute Nodes are estimated to sustain 1 PetaFLOP running on 1,000,000 CPUs. Compute Node Costs · Hardware A Compute Node's hardware costs less than $1,000. 25 Compute Nodes containing 1,500 total CPUs cost approximately $15,000 in hardware per availability. · Operation Operating costs are primarily determined by software and hardware engineering labor. Overall Assessment · Summary Overall performance has met high expectations with no technical issues. The current architecture has proven to be a scalable and adaptive computational research platform for machine learning and life science.
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