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June 22, 2011

Practicalities and Challenges in the Petaflops Era

Thomas Sterling and Chirag Dekate

Every year at ISC we stop and look back at the field of HPC, which has consistently exhibited the greatest rate of change of any technology in the history of mankind. This year is particularly important as the conventional methods that have served well over the last two decades are in direct contention with the technology trends pushing us towards a new future. This is best highlighted in the context of petaflops-capable supercomputers that have become the new standard at the top end of HPC and the reemergence of Asia as a dominant player in that ethereal regime.

But what has defined this year and distinguished it from the recent past is that although the issues are clear, the future conclusions are not. Perhaps, that is the lesson: that we are in a rare state of transition the outcome of which is yet to be determined. And the debate is anything but over. Let’s consider the highlights.

Petaflop computing is now the norm worldwide with the US, Europe, and Asia all driving computation beyond 10^15 flops. Most notable was China with its deployment of Tianhe-1A exceeding 2.5 petaflops (Linpack), assuming the position of the “fastest computer in the world” in 2010. That system is now surpassed with the 8 petaflops K Computer from Japan, giving that country the top spot for the first time since the illustrious Earth-Simulator.

Asia also has significant deployment of more traditional HPC systems, providing the means for strong programs in computational science with potential long-term impact on future science and engineering disciplines. Finally, an increasing share of the integrated components in Asia is homegrown, indicating a likely future with fully native HPC systems.

This year the big debate is the future of HPC system architecture: homogeneous multicore/manycore or heterogeneous GPU-based structures. And in both cases, the issue of programming dominates. GPUs are perceived by many as the fast track to superior computing. And for some applications this has been demonstrated. Indeed, of the top four machines, three incorporate GPUs. That would suggest a clear trend. But not so fast. Of the top 500 systems, only 17 integrate GPUs as a seminal element in achieving their performance goals. That would also suggest a clear trend, but in the opposite direction.

GPUs bring an enormous combined floating-point capability in a relatively small package and at a superior power/performance envelope. The numbers are staggering, but at a cost. Sitting at the wrong end of a PCI bus, the long latencies and relatively low bandwidth demands very high data reuse and highly regular control flow to extract anything near their peak potential. And with program control residing with the general-purpose processors, the programming methods for such hybrid systems is not for the faint of heart or consistent with the mass of legacy codes upon which industry, science, and governments all rely upon and have invested in.

Thus, it is possible that such architectures as TSUBAME 2.0 are transitional in that they represent the beginnings of an empirical search that in a few years will resolve in a distinctly different system architecture, exploiting the best of both manycore and GPUs but in a balanced and well-integrated structure managed by a unified programming methodology. While many practitioners experiment, sometimes to good effect, with CUDA and the emerging OpenCL framework, many more codes and programmers remain wedded to more day-to-day productive means.

These are very exciting times but those who think they know the final answer are probably fooling themselves, if not the rest of us. After all, the new number one K supercomputer is not based on GPUs but is 3 times faster than the number two Tianhe-1A machine, which is.

The steady increase in delivered performance is also pushing the power envelope. One advantage of GPUs, when employed effectively, is a somewhat improved energy efficiency (joules/operations). But while clock rates remain relatively stable (although differing across a range of approximately 3X) the scale of the largest systems continues to grow as HPC approaches another milestone: a million cores. The tradeoff is complex, but grave concerns are warranted as the biggest machines top 10 megawatts.

This is the driving and principal constraint for ambitious projects to deliver sustained exaflops performance before the end of this decade. The International Exascale Software Project has a worldwide representation coordinating the development of a new software platform that will support exascale systems in their management and application in the next decade. Recognizing the long lead times for software and their corresponding almost prohibitive costs, the opportunity to combine investment of resources in mutually aligned directions would appear to be an essential strategy to achieve billion-way parallelism.

In the US, the DARPA sponsored UHPC program, while not expressly targeting exascale systems has initiated this year to develop suitable technologies for a petaflop in a rack at under 60 kilowatts. The European Exascale Software Initiative is to develop a roadmap to exaflops, and also in Europe, both Intel and separately, Cray, are engaged in collaborations with European researchers to drive towards exaflops. In Asia, both Japan and China have programs intended to move aggressively towards sustained exaflops for real world applications, perhaps as early as 2018. But with predictions of hundreds of megawatts required through extensions of conventional methods, what such systems will look like is far from certain, let alone how they will be programmed.

Driving the field of HPC towards new capabilities is the underlying technologies and the processor designs from which they are constructed. Intel, IBM, and AMD are all advancing their processor designs. 45 and 32 nanometer technologies are taking hold even as the number of cores per die and socket is increasing to deliver continuing increase in performance.

Intel’s Xeon E7-8870 Processor integrates 10 cores, operating at 2.7 GHz, with 30MB cache size and supporting 2 terabytes of DDR3 memory. Using Hafnium-based high-k metal gate silicon technology, the Intel chip burns 130 Watts.

Cooler is the 12-core 2.5 GHz AMD Opteron 6100 component at 45 nanometers. It draws 105 Watts and is based on their full-field EUV lithography technology. AMD plans on going to 16 cores by Q3 of this year based on 32 nanometers, while Intel is preparing their 22 nanometer Ivy-bridge processors based on 3-D TriGate transistors.

IBM’s heavy hitter continues to be the Power family with the 45 nanometer Power7 out last year, supporting a number of chip configurations between 4 and 8 cores. This will serve as the central component to the 10 petaflops Blue Waters machine to be deployed next year. Its successor, IBM Power8, is currently under development.

GPU designs continue to push the edge of the envelope in peak performance while enhancing their generality for greater utility. The NVIDIA Tesla 20-series family based on the Fermi architecture can integrate up to 512 CUDA cores with clock rates of between 1.15 and 1.4 GHz and deliver more than a half a teraflop of double precision performance. With comparable performance is the AMD FireStream 9370 series GPU based on the Cypress architecture. Both vendors are moving towards tighter system integration with AMD’s pushing its Fusion system architecture. In the software domain, it’s a head-to-head fight between CUDA and OpenCL, with strong advocates for each.

The underlying technologies are certainly not standing still. Recent graphene technology breakthroughs include UCLA reporting 300 GHz switching rates and UC Berkeley announcing new optical modulators, while IBM has implemented the first integrated circuit based on graphene transistors. 3-D stacking of dies by IBM, Xilinx, and other manufacturers is preparing HPC for higher density packaging with higher internal bandwidths and shorter latencies, while combining disparate functional components (e.g., cores, DRAM) into single integrated units.

Every year an attempt is made to capture a more meaningful representation of supercomputing based on the TOP500. The list provides extensive data but usually only discussed in terms of the highest rated machine, the lowest rated machine, and the sum of all 500 machines. But what about supercomputing for the common man; the mainstream form and capability. This year, although the top machines exhibit unique properties, the canonical system is the standard Linux commodity cluster with a peak performance of 72.4 Teraflops and a Linpack rating of 38.3 teraflops. Such a system incorporates Intel Xeon Nehalem-EP processors, integrated by IBM (HP is a close second), and interconnected with Gigabit Ethernet (InfiniBand has almost caught up). The system comprises 1,134 sockets of 6 cores each and burns 200 Kilowatts. The closest machine to this profile is number 288 on the TOP500 list.

Even though we still rate systems in teraflops, the Graph 500 list is emerging to represent a very different class of computing: data intensive processing, a domain in which the manipulation of the metadata dominates in lieu of floating point operations. Although yet to dominate, this emerging class of computing is important for many sparse problems as well as knowledge management and understanding problems that are expected to have increasing impact on the field of HPC.

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