1. Alternative Processors 5/22/20151 John Gustafson CEO, Massively Parallel Technologies (Former CTO, ClearSpeed)

2. What is an Alternative Processor 5/22/20152 A processor (and local memory) specialized to a task that otherwise would be handled by a mainstream CPU Two kinds: Invisible (like disk controllers, GPUs used for graphics) Visible (like network accelerators, FPGAs, GPUs made accessible for programming) Superior adaptation to a task can improve Performance per watt Performance per liter of system volume Performance per dollar

3. Memory Structure 5/22/20153 A big advantage for HPC is the access to a separate memory space that sacrifices generality for speed NVIDIA and ClearSpeed can offer 100s of GBytes/sec (Gbyps) for vector-type operations on small RAM… that’s far more important than any peak Gflops capability. x86 cache is very wasteful, especially for HPC. Only about 20% of transfers actually wind up being used! And x86 cache is highly optimized for non-HPC applications. HPC library programmers know how to exploit an explicitly-managed hierarchical memory.

4. How Should They Be Used? 5/22/20154 ~90% of users should never have to program them, at all. They should be invisible. ~10% of users can justify altering code to use alternative library calls and overlapping execution. <1% of users should ever muck with the direct native programming of an alternative processor.

5. What Language Should be Used? 5/22/20155 This is a question only for that 10% of programmers category mentioned on the last slide. (For the <1%… C or assembler!) It’s a makefile issue, not a language issue! We already conceal massive amounts of system management complexity in the makefiles. The last thing we need is a new language. We need better ways to extend existing languages, carefully. (And yes, a ‘wrapper’ standard like Exochi or CUDA might help.) One line of tested, debugged, documented code costs about $50 to $100. Simple budget arguments show why we have code inertia.

6. Accelerating the Right Thing 5/22/20156 Folks… why are we still trying to accelerate computation? It’s already hundreds of times faster than the communication! <1% of all HPC applications are still compute-bound (not counting the LINPACK runs done for press releases). The rest are bound by memory bandwidth, inter-server communications, and disk I/O. Massively Parallel Technologies is focused on the >99% of HPC applications that are communication-bound. This is the future of alternative processors. And the ‘alternative’ must have as little impact on users and programmers as possible.