Inside Tsubame, Japan's GPU-Based Supercomputer 75
Startled Hippo writes "Japan's Tsubame supercomputer was ranked 29th-fastest in the world in the latest Top 500 ranking with a speed of 77.48T Flops (floating point operations per second) on the industry-standard Linpack benchmark. Why is it so special? It uses NVIDIA GPUs. Tsubame includes hundreds of graphics processors of the same type used in consumer PCs, working alongside CPUs in a mixed environment that some say is a model for future supercomputers serving disciplines like material chemistry." Unlike the GPU-based Tesla, Tsubame definitely won't be mistaken for a personal computer.
Re:Hold the hyperbole (Score:4, Insightful)
That's why the supercomputer rankings are based on reasonably complex benchmarks instead of synthetic "cores * flops/core" types of numbers. Scoring well on the benchmark is supposed to be solid evidence that the computer can in fact do something useful. My question though is whether the GPUs contributed to the benchmark score, or were just along for the ride.
Re:Could do it for cheaper (Score:3, Insightful)
They could do it cheaper with anything at the current price. However, this wasn't just slopped together last month with the latest hardware off newegg.
No doubt, there's a SC being built up right now around all the latest AMD parts. By the time it gets benchmarked, we'll be able to complain that something else is a better deal.
Re:Hold the hyperbole (Score:4, Insightful)
how would data parallelism negatively affect a test that is designed to measure a system's performance in supercomputing applications--a field which is dominated by problems which involve processing extremely large data sets?
if vector processors do in fact perform poorly on LINPACK benchmarks then that would mean LINPACK performance is not a good indicator of real-world performance, but that clearly isn't the case as vector processors consistently perform quite well in LINPACK suite measurements [hoise.com].
vector processing began in the field of supercomputing, which during the 1980's and 1990's were essentially the exclusive realm of vector processors. it wasn't until companies, to save money, started designing & building supercomputers using commodity processors (P4s, Opterons, etc.) that general-purpose scalar CPUs began to replace specialized vector processors in high-performance computing. but now companies like Cray and IBM [cnet.com] are starting to realize that this change was a mistake.
even in commodity computing the momentum is shifting away from general-purpose scalar CPUs towards specialized vector coprocessors like GPUs, DSPs, array processors, stream processors, etc. when you're dealing with things like scientific modeling, economic modeling, engineering calculations, etc. you need to crunch large data sets using the same operation; this is best done in parallel using SIMD. using specialized vector processors (and instruction sets) you can run these applications far more efficiently than you could using a scalar processor running at much higher clock speeds. the only downside is that you lose the advantage of using commodity hardware that's cheap because of their high volume production. but if companies like Adobe start developing their applications to employ vector/stream coprocessors, then that will boost the adoption of these vector processors in the commodity computing market, which will increase production volume and lower manufacturing costs.