Bursting the HPC Bubble

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Bursting the HPC Bubble


Traditionally, excessive efficiency computing has operated in a closed loop, enclosed in its personal software program cocoon. Libraries like MPI, OpenACC, and BLAS, are barely recognized exterior the confines of the HPC world.

That is primarily as a result of excessive efficiency computing was historically carried out on equipment that had little in frequent with the remainder of the business and was utilized to the sorts of science and engineering simulations that had distinctive computational calls for with regard to each efficiency and computational scale.

Customized-built HPC {hardware} largely fell by the wayside 30 years in the past, when commodity clusters turned all the craze.  And now, with the appearance of large-scale cloud computing and performance-demanding purposes like deep studying, the unique nature of HPC software program is fading away. After all, there’s sure to be some resistance to this from die-hard supercomputing fanatics, however a brand new technology is paving the way in which for a extra inclusive strategy.

One in all them is Michela Taufer, a pc scientist who heads the International Computing Laboratory (GCLab) on the College of Tennessee, Min H. Kao Division of Electrical Engineering & Pc Science. Taufer, an ACM Distinguished Scientist, can also be the overall chair of this 12 months’s supercomputing extravaganza, SC19. Her day job working GCLab includes managing the varied tasks underneath the lab’s purview, in addition to the scholars who work there.

A major analysis space on the lab is information analytics know-how, a lot of which originated exterior the excessive efficiency computing neighborhood, however has been subsequently modified for HPC environments. A telling instance of this strategy is Mimir, a MapReduce implementation that makes use of MPI. In contrast to the standard cloud computing model upon which it’s primarily based, Mimir is designed with scalability in thoughts and is designed to run on the biggest supercomputers. It additionally exploits in-memory processing and information staging to maximise efficiency on the node degree. It may be accessed by way of GCLab’s GitHub software program portal.

We acquired the chance to talk with Taufer to get her sense of how the sphere is altering, particularly how information analytics is remodeling HPC workflows and the way the software program is being formed by exterior forces, particularly the deep studying neighborhood.

One of many large drivers for these adjustments, she says, the rising significance of knowledge to HPC workflows. In some methods, it’s a delicate change, inasmuch as information all the time drove simulations and the ensuing visualizations of these simulations. However a lot of at present’s workflows are an excellent deal extra advanced, which has tended to shift the emphasis from compute to information. A part of that is only a philosophical shift, predicated on the idea that there’s data embedded in information that may be extracted with the best instruments. That’s the place deep studying has change into so useful, she says.

A extra apparent clarification of this newfound appreciation for information is that there are merely extra gadgets like distant sensors and different varieties of scientific devices for gathering bits and bytes which can be being fed into HPC programs.  Taufer says meaning we have to “shed our reliance on these large simulations,” and increase our workflows past the supercomputing heart. Integrating these exterior information streams into our fashions is large problem, she admits, however one we have to pursue.

The opposite important change to workflows is the incorporation of deep studying, which from Taufer’s perspective is simply one other information analytics approach. Deep studying will be utilized to completely different elements of the HPC workflow, mostly to assist filter uncooked enter information and to information simulations extra intelligently by lowering the parameter house.

Most simulations contain searches of 1 type or one other, explains Taufer. That may imply finding an anomalous sign from a sensor community, in search of the signature of an atomic particle decaying, or discovering the seismic hint that signifies an oil deposit. Utilizing deep studying to streamline these simulations means much less time is spent do brute-force computations, making the entire course of an excellent deal extra environment friendly.

In line with Taufer, this know-how has particular worth within the space of molecular dynamics, a computational methodology that may be utilized to essential well being purposes like drug design, illness research, and precision medication. These flops-demanding simulations usually require modern supercomputers to create helpful fashions, so lowering the computational burden with deep studying can open up these areas to researchers with extra modest sources.

One in all GCLab’s tasks includes in-situ information evaluation of molecular dynamics information in flight, utilizing machine studying and different analytics approaches to hurry the workflow. In different instances, comparable to DeepMind’s AlphaFold utility for protein folding, machine studying has truly made the simulation superfluous.

Her normal sense is {that a} small neighborhood like HPC advantages from a collaborative strategy and one that’s interdisciplinary in nature. Bringing collectively pc scientists, engineers and area specialists isn’t solely advantageous to most HPC challenge as of late, it’s usually obligatory. Fortuitously, the scholars she mentors in her lab appear extra open to this sort of strategy and fewer inclined to work within the software program silos that outlined HPC of years previous. “We really feel that that is the best approach to transfer ahead,” says Taufer.

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