• DSS v0.6 - CentOS 7.8.2003 (kernel 3.10.0-1127.el7.x86_64) • NFS v4 – Ubuntu 20.04.3 LTS (kernel 5.4.0-100-generic)It was important to compare the solution not just at the storage layer, but also at the application layer (i.e. AI training). The team leveraged an AI benchmarking tool for storage solutions utilizing Tensorflow and PyTorch – two well-known AI frameworks – to measure storage performance in terms of data load time, aggregated listing time, throughput, latency and other parameters against the customer’s AI training algorithm and dataset. The number of AI training instances per client node was varied to demonstrate performance as parallel workloads increased. The graph below illustrates the results:
• Perform additional large-scale testing with different data parameters (dataset size, variety, number of client nodes, etc.). • Test with GPU servers performing actual machine learning operations, as AI/ML training workloads are not like other workloads. • Increase SSD capacity/host in one of two ways: ◦ Increase SSD capacity from 32 to 64 TB. ◦ Find servers that will use 24 or more SSDs rather than only 16 which can be challenging. • Upgrade to new CPU generations to test the ceiling to determine the impact of new speeds provided by Samsung’s next-generation PCIe Gen 5 SSDs and DDR5 memory on the servers.While there are still some unknowns, the ability of DSS to deliver high-performance throughput for extremely large-scale, data-intensive workloads, regardless of the hardware configuration, is quite promising. Learn More If you are interested in taking a closer look at this architecture, we recommend you visit https://github.com/OpenMPDK/DSS↗. Stay tuned for new results as additional parameters are tested. For more information about Samsung’s market-leading memory solutions, visit https://semiconductor.samsung.com/emea.
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