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OMRF Selects WekaIO Data Platform to Accelerate Drug Discovery

CAMPBELL, Calif., March 2, 2021

With Weka’s Limitless Data Platform, scientists can focus on disease prevention and treatment rather than worrying about their data storage.

WekaIO™ (Weka), the fastest-growing data platform for artificial intelligence/machine learning (AI/ML), life sciences research, and high-performance data analytics (HPDA), today announced that the Oklahoma Medical Research Foundation (OMRF) has implemented Weka’s Limitless Data Platform™ to simplify, speed up, and scale their mixed workloads. By using Weka, OMRF can run more and concurrent research jobs in a shorter amount of time.

Established in 1946, OMRF is an independent, nonprofit biomedical research institute with more than 450 staff and over 50 labs studying cancer, heart disease, autoimmune disorders, and diseases of aging. Discoveries at OMRF led to the first targeted therapy approved in the U.S. for sickle cell disease and the first approved treatment for neuromyelitis optica spectrum disorder, a rare autoimmune disease.  OMRF’s critical research is helping people live longer, healthier lives, one discovery at a time.

The challenge for OMRF was to architect a system for scientists that would satisfy their growing informatics needs for their research: more compute, more storage, faster storage, for bigger data sets.  By implementing Weka, OMRF was able to:

“Our scientists can more easily complete their analysis and no longer have to deal with the storage management and life cycle,” said Stuart Glenn, Assistant Director of Infrastructure and Research Computing at OMRF. “Our old workflow was complex and time-consuming, involving staging data to a compute node’s local SSD for better application performance. With Weka, we have so much performance and expandable capacity that we don’t have to think about it.”

After considering other products from their legacy storage vendor, all-flash solutions, and BeeGFS- based systems, OMRF decided to deploy Weka on Supermicro hardware to achieve a fast and cost-efficient storage system that enables it to focus on saving lives rather than focusing on storage.

“Organizations have a large number of options when it comes to choosing a storage solution, but only Weka can simply and cost-efficiently manage mixed workloads at scale on a single platform without any impact to performance,” said Ken Grohe, president and CRO at WekaIO.  "The reduction in job run time that OMRF achieved with Weka will have a significant, positive impact on their research outputs. Working with partners like Supermicro and Silicon Mechanics, the Weka Limitless Platform delivered the benefits and return on investment OMRF needed to stay on the forefront of scientific discovery.”

With Weka’s data platform, OMRF research jobs were reduced 10X, with one job reduced from 70 days to 7 days; another common analysis workflow was reduced from 12 hours to 2 hours. They can now support multiple and simultaneous new research initiatives; faster turn-around time and jobs finishing faster gets results to researchers faster.

Weka solves the storage challenges common with I/O-intensive workloads delivering more enterprise features to store, protect, and manage data. Delivered as a broad solution for many use cases through the Weka Innovation Network™ (WIN), the Limitless Data Platform is built on the Weka File System (WekaFS™), a software-defined architecture that delivers the industry’s best performance and efficiencies by leveraging the latest technologies in storage such as NVMe, networking technologies like NVMe-oF, 100Gb Ethernet, and advances in computing technologies like GPU acceleration.

“Silicon Mechanics has been working with OMRF for years on various HPC systems so we knew that the massively parallel-architecture of WekaFS and its ability to seamlessly tier to object storage would be a great fit for OMRF,” said Matt Ritter, director of engineering at Silicon Mechanics, a WIN Leader Partner. “We can design and build the best HPC compute infrastructure in the world, but it won’t meet our client’s needs without a top-notch, integrated storage component. That’s why we always leverage storage technology that provides the I/O throughput necessary to get the job done. With Weka, OMRF has the solution it needs to be successful.”

“We are pleased that OMRF was able to take advantage of Supermicro Storage Solution Bundle featuring WekaFS, which is preconfigured and optimized for maximum compute acceleration and reduced job times, delivering unmatched performance at scale,” said Michael McNerney, vice president of marketing and networks security at Supermicro, a WIN Innovation Partner. “Our joint solution is currently available in a set of turn-key appliances that easily satisfy any performance, capacity, and footprint requirements.”

For more information about Weka’s Limitless Data Platform, go to: https://www.weka.io, and to request a free trial of WekaFS, go to Get Started with Weka.

To find a WekaFS authorized reseller in the Weka Innovation Network™ (WIN), go to https://www.weka.io/partners.

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About WEKA
WEKA is the AI data and memory infrastructure company transforming the economics of agentic AI. Its WEKA® NeuralMesh™ platform unifies high-performance data storage with extended GPU memory, giving enterprises, AI cloud providers, and AI builders a single foundation for training, inference, and agentic workloads. With Augmented Memory Grid™, NeuralMesh extends GPU memory capacity by 1000x, accelerates time to first token by up to 20x, and delivers 10x more concurrent users from the same GPU footprint, proven in production benchmarks. Trusted by 30% of the Fortune 50, WEKA enables organizations to scale AI faster, optimize GPU utilization, and reduce the cost of every token served. Learn more at www.weka.io or connect with us on LinkedIn and X.

WEKA and the WEKA logo are registered trademarks, and WEKA NeuralMesh and Augmented Memory Grid are trademarks of WekaIO, Inc. Other trademarks mentioned herein are the property of their respective owners.

Media Contact:
WEKA Communications
pr@weka.io

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