Lenovo EveryScale Design Architecture for WEKA Storage
In this solution brief, you'll discover how Lenovo EveryScale for WEKA storage can be planned and implemented to deliver maximum performance. Review the document to learn more.
What is the WEKA Data Platform?
The WEKA Data Platform is designed to tackle the storage challenges faced by modern data-intensive applications, such as those used in artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC). It simplifies storage management by eliminating the complexities associated with legacy storage solutions while still offering essential enterprise features at a lower cost. This platform is tailored to meet the rigorous storage demands of data-heavy workloads, enabling faster insights from large datasets.
How does core allocation affect performance?
Dedicating cores to the WEKA client can significantly enhance performance. The number of dedicated cores, typically 1 or 2, should be chosen based on the client's network bandwidth and I/O requirements. While these dedicated cores are not available for other computational tasks, modern HPC systems usually have high core counts, making this allocation manageable. Proper configuration ensures that the WEKA client operates at optimal performance levels.
What networking configurations are recommended?
For optimal performance with WEKA, it is advisable to utilize RDMA-enabled network adapters, such as NVIDIA Networking VPI adapters. When mounting the storage system, the name of the RDMA adapter should be included in the client mount options. In a Lenovo ThinkSystem SR630 V2 server, placing two RDMA adapters in specific slots can maximize performance by ensuring efficient connectivity across NUMA domains.
Lenovo EveryScale Design Architecture for WEKA Storage
published by Clear Technologies
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