Scalable Systems Research Labs Inc.

SSRLabs is the global leader in instruction- and energy-efficient massively parallel coprocessors to accelerate HPC and Big Data applications.

Challenges in HPC and Big Data

High Performance Compute (HPC) is broken - users and programmers agree. The same is true for Big Data. Billions of Dollars are being invested in solving it in software. We still have not seen a solution to the problem as it is. Our assumption is that software won't solve HPC and Big Data challenges - our accelerators in conjunction with easy-to-use and fully standardized APIs such as openACC and openCL will.

A quick look at the world's fastest supercomputers reveals a number of issues. Peak theoretical performance and measured performance differ quite substantially. For BLAS, an embarrassingly parallel problem, the efficiency on Tianhe-2 is a mere 62%, and for other computational workloads the efficiency is even lower. However, among supercomputers this is one of the better levels of efficiency. Other supercomputers fare far worse - particularly those that deploy SIMD accelerators such as GPGPUs. Simple meshes inside accelerators don't work well either, as Tilera's lack of success has demonstrated.

If we have a look where we are at today and where the DOE's ExaFLOPS Challenge wants the HPC industry to be, let's just look at the numbers. Today's highest performing supercomputer is Tianhe-2 with about 34 TFLOPS of numeric performance at a power consumption of roughly 18 MW. That turns out to deliver about 1.889 MFLOPS/W. In other words, Tianhe-2 delivers nearly 2 million floating-point operations per second per Watt of electricity it consumes, running BLAS as a benchmark. The DOE asks for 1 ExaFLOPS (that is 10^18 floating-point operations per second) at a total allowable power consumption of 20 MW, and presumably for a more normalized mix of benchmarks. That boils down to 50,000 MFLOPS/W. In other words, the energy-efficiency of today's supercomputers must improve by a factor of more than 25,000 to fulfill the ExaFLOPS Challenge. Not even Moore's Law - if we assume it will continue to be true - will afford us that until the 2020 deadline. It is clear that simply banking on Moore's Law won't get us there. Architectural changes are required, and that is what SSRLabs does.

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2015-01-26: SSRLabs has signed contracts for international expansion.

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No SIMD