Company Contributes to Comprehensive Set of Rules to Measure System Performance
WekaIO, the innovation leader in high-performance, scalable file storage for data intensive applications, announced that it has become a supporting organization of the MLPerf benchmark suite. Alongside companies like NVIDIA, NetApp and Qualcomm, WekaIO will contribute its expertise to efforts to define the machine learning (ML) benchmarks for the industry.
“We have demonstrated that WekaIO Matrix™ delivers superior performance benefits and is uniquely positioned to be the best storage solution for ML workloads”
MLPerf’s objective is to build a common set of benchmarks that enables the ML field to measure system performance for both training and inference from mobile devices to cloud services. The organization is a consortium of researchers from prestigious universities in the United States along with leaders in ML such as Baidu, Intel, Google and others. The recently released MLPerf v0.5 benchmark is the industry’s first objective performance test for training algorithms to perform tasks such as computer vision, language translation, personalized recommendations, and reinforcement learning.
“We have demonstrated that WekaIO Matrix™ delivers superior performance benefits and is uniquely positioned to be the best storage solution for ML workloads,” said Barbara Murphy, Vice President of Marketing at WekaIO. “Our customers are using Matrix™ to accelerate ML workloads at scale. Matrix is the only file system today that can keep GPUs and CPUs saturated with data, eliminating the IO constraints commonly associated with legacy storage systems. For successful ML outcomes, it’s critical for organizations to consider the underlying storage infrastructure. So, we are honored to contribute our knowledge and proficiency to the MLPerf benchmark which, when established, will help organizations make better informed infrastructure decisions.”
In addition, WekaIO participates in the well-established SPEC SFS benchmark—a standard for high-performance computing (HPC) performance—and a proponent of the importance of delivering performance standards for the ML and HPC industry at large.