In a partnership with Intel and Cray, Lawrence Livermore National Laboratory revealed a high performance computing cluster called "Catalyst" which will be deployed later this month using solid state drives as an alternative to hard drives and DRAM, which will likely help speed up internal data transfers. This cluster will serve research scientists at all three institutions, with access rights based on the level of investment.
"As the name implies, Catalyst aims to accelerate HPC simulation and big data innovation, as well as collaborations between the three institutions," said Matt Leininger, deputy of Advanced Technology Projects for LLNL. "The partnership between Intel, Cray and LLNL allows us to explore different approaches for utilizing large amounts of high performance non-volatile memory in HPC simulation and Big Data analytics."
According to a list of specs, the cluster has a performance of 150 trillion floating operations per second, backed by 324 nodes and 7,776 cores based on the latest-generation 12-core Intel Xeon E5-2695v2 processors. Catalyst also runs the NNSA-funded Tri-lab Open Source Software (TOSS), according to LLNL, providing a common user environment across NNSA Tri-lab clusters.
Catalyst also uses 128 gigabytes (GB) of dynamic random access memory (DRAM) per node, 800 GB of non-volatile memory (NVRAM) per compute node, 3.2 terabytes (TB) of NVRAM per Lustre router node, and improved cluster networking with dual rail Quad Data Rate (QDR-80) Intel TrueScale fabrics. Catalyst also uses an expanded node local NVRAM storage tier based on PCIe high-bandwidth Intel Solid State Drives (SSD).
"Big Data unlocks an entirely new method of discovery by deriving the solution to a problem from the massive sets of data itself. To research new ways of translating Big Data into knowledge, we had to design a one-of-a-kind system," said Raj Hazra, Intel vice president and general manager of the Technical Computing Group. "Equipped with the most powerful Intel processors, fabrics and SSDs, the Catalyst cluster will become a critical tool, providing insights into the technologies required to fuel innovation for the next decade."
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LLNL's Donald Johnson said that Catalyst's increased storage capacity in both volatile and nonvolatile memory represents a major departure from classic simulation-based computing architectures common at DOE laboratories. The configuration should open new opportunities for exploring the potential of combining floating-point-focused capability with data analysis in one environment. The architecture should also provide insights into the kind of technologies the ASC program will require over the next 5-10 years to meet high performance simulation and big data computing mission needs, he said.
"The machine's expanded DRAM and fast, persistent NVRAM are well suited to big data problems (i.e, bioinformatics, business analytics, machine learning and natural language processing), as well as meeting the increasingly demanding simulation requirements of ASC. Catalyst should extend the range of possibilities for the processing, analysis and management of the ever larger and more complex datasets that many areas of business and science now confront," Johnson said.
Catalyst is expected to be available for limited use this month and general use by December.