Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a community infrastructure in support of machine learning research. The ambitious plan – Cognitive Hardware and Software Ecosystem, Community Infrastructure ( #CHASECI) – is intended to leverage the high-speed Pacific Research Platform (PRP) and put fast GPU appliances into the hands of researchers to tackle machine learning hardware, software, and architecture issues. Given the abrupt rise of machine learning and its distinct needs versus traditional FLOPS-dominated HPC, the CHASE-CI effort seems a natural next step in learning how to harness PRP’s high bandwidth for use with big data projects and machine learning. Perhaps not coincidentally Smarr is also principal investigator for PRP. As described in the NSF abstract, CHASE-CI “will build a cloud of hundreds of affordable Graphics Processing Units (GPUs), networked together with a variety of neural network machines to facilitate development of next generation cognitive computing.” Those are big goals. Last week, Smarr and co-PI Thomas DeFanti spoke with HPCwire about the CHASE-CI project. It has many facets. Hardware, including von Neumann (vN) and non von Neumann (NvN) architectures, software frameworks (e.g., Caffe and TensorFlow), six specific algorithm families (details near the end of the article), and cost containment are all key target areas. In building out PRP, the effort leveraged existing optical networks such as GLIF by building termination devices based on PCs and providing them to research scientists. The new device — dubbed FIONA (Flexible I/O Network Appliances) – was developed by PRP co-PI Philip Papadopoulos and is critical to the new effort. A little background on PRP may be helpful.  Larry Smarr, director, Calit2 As explained by Smarr, the basic PRP idea was to experiment with a cyberinfrastructure that was appropriate for a broad set of applications using big data that aren’t appropriate for the commodity internet because of the size of the of the datasets. To handle the high speed bandwidth, you need a big bucket at the end of the fiber notes Smarr. FIONAs filled the bill; the devices are stuffed with high performance, high capacity SSDs and high speed NICs but based on the humble and less expensive PC. “They could take the high data rate without TCP backing up and thereby lowering the overall bandwidth, which traditionally has been a problem if you try to go directly to spinning disk,” says Smarr. Currently, there are on the order of 40 or 50 of these FIONAs deployed across the West Coast. Although 100 gigabit throughput is possible via the fiber, most researchers are getting 10 gigabit, still a big improvement. DOE tests the PRP performance regularly using a visualization tool MadDash (Monitoring and Debugging Dashboard). “There are test transfers of 10 gigabytes of data, four times a day, among 25 organizations, so that’s roughly about 300 transfers four times a day. The reason why we picked that number, 10 gigabytes, was because that’s the amount of data you need to get TCP up to full speed,” says Smarr.  Thomas DeFanti, co-PI, CHASE-CI Networks are currently testing out at 5, 6, 7, 8 and 9 gigabits per second, which is nearly full utilization. “Some of them really nail it at 9.9 gigabits per second. If you go to 40 gigabit networks that we have, we are getting 13 and 14 gigabits per second and that’s because of the [constrained] software we are using. If we go to a different software, which is not what scientists routinely use [except] the high energy physics people, then we can get 30 or 40 or 100 gigabits per second – that’s where we max out with the PC architecture and the disk drives on those high end units,” explains DeFanti. The PRP has proven to be very successful, say Smarr and DeFanti. PRP v1, basically the network of FIONAs, is complete. PRP v2 is in the works. The latter is intended to investigate advanced software concepts such as software defined networking and security and not intended to replace PRP v1. Now, Smarr wants to soup up FIONAs with FPGAs, hook them into the PRP, and tackle machine learning. And certainly hardware is just a portion of the machine learning challenge being addressed.
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