A new programming language for hardware accelerators


Researchers created Exo, which helps efficiency engineers remodel easy applications that specify what they wish to compute into very advanced applications that do the identical factor because the specification, solely a lot, a lot quicker. Credit score: Pixabay/CC0 Public Area

Moore’s Legislation wants a hug. The times of stuffing transistors on little silicon pc chips are numbered, and their life rafts—{hardware} accelerators—include a value.

When programming an accelerator—a course of the place purposes offload sure duties to system particularly to speed up that job—it’s a must to construct an entire new software program help. {Hardware} accelerators can run sure duties orders of magnitude quicker than CPUs, however they can’t be used out of the field. Software program must effectively use accelerators’ directions to make it appropriate with your complete software system. This interprets to quite a lot of engineering work that then must be maintained for a brand new chip that you simply’re compiling code to, with any programming language.
Now, scientists from MIT’s Laptop Science and Synthetic Intelligence Laboratory (CSAIL) created a brand new known as “Exo” for writing high-performance code on {hardware} accelerators. Exo helps low-level efficiency engineers remodel quite simple applications that specify what they wish to compute, into very advanced applications that do the identical factor because the specification, however a lot, a lot quicker through the use of these particular accelerator chips. Engineers, for instance, can use Exo to show a easy matrix multiplication right into a extra advanced program, which runs orders of magnitude quicker through the use of these particular accelerators.
Not like different programming languages and compilers, Exo is constructed round an idea known as “Exocompilation.” “Historically, quite a lot of analysis has centered on automating the optimization course of for the particular {hardware},” says Yuka Ikarashi, a Ph.D. scholar in and pc science and CSAIL affiliate who’s a lead creator on a brand new paper about Exo. “That is nice for many programmers, however for efficiency engineers, the compiler will get in the way in which as typically because it helps. As a result of the compiler’s optimizations are automated, there is not any good solution to repair it when it does the improper factor and provides you 45 % effectivity as an alternative of 90 %.”
With Exocompilation, the efficiency engineer is again within the driver’s seat. Duty for selecting which optimizations to use, when, and in what order is externalized from the compiler, again to the efficiency engineer. This manner, they do not should waste time preventing the compiler on the one hand, or doing all the pieces manually on the opposite. On the similar time, Exo takes duty for guaranteeing that every one of those optimizations are appropriate. In consequence, the efficiency can spend their time enhancing efficiency, reasonably than debugging the advanced, optimized code.

“Exo language is a compiler that is parameterized over the {hardware} it targets; the identical compiler can adapt to many alternative {hardware} accelerators,” says Adrian Sampson, assistant professor within the Division of Laptop Science at Cornell College. “As a substitute of writing a bunch of messy C++ code to compile for a brand new accelerator, Exo offers you an summary, uniform solution to write down the ‘form’ of the {hardware} you wish to goal. Then you may reuse the present Exo compiler to adapt to that new description as an alternative of writing one thing completely new from scratch. The potential impression of labor like that is monumental: If {hardware} innovators can cease worrying about the price of growing new compilers for each new {hardware} concept, they’ll check out and ship extra concepts. The business might break its dependence on legacy {hardware} that succeeds solely due to ecosystem lock-in and regardless of its inefficiency.”
The very best-performance pc chips made in the present day, corresponding to Google’s TPU, Apple’s Neural Engine, or NVIDIA’s Tensor Cores, energy and machine studying purposes by accelerating one thing known as “key sub-programs,” kernels, or high-performance computing (HPC) subroutines.
Clunky jargon apart, the applications are important. For instance, one thing known as Fundamental Linear Algebra Subroutines (BLAS) is a “library” or assortment of such subroutines, that are devoted to linear algebra computations, and allow many machine studying duties like neural networks, climate forecasts, cloud computation, and drug discovery. (BLAS is so necessary that it gained Jack Dongarra the Turing Award in 2021.) Nevertheless, these new chips—which take a whole bunch of engineers to design—are solely pretty much as good as these HPC software program libraries enable.
At present, although, this type of efficiency optimization continues to be completed by hand to make sure that each final cycle of computation on these chips will get used. HPC subroutines often run at 90 percent-plus of peak theoretical effectivity, and {hardware} engineers go to nice lengths so as to add an additional 5 or 10 % of pace to those theoretical peaks. So, if the software program is not aggressively optimized, all of that arduous work will get wasted—which is precisely what Exo helps keep away from.
One other key a part of Exocompilation is that efficiency engineers can describe the brand new chips they wish to optimize for, with out having to switch the compiler. Historically, the definition of the {hardware} interface is maintained by the compiler builders, however with most of those new accelerator chips, the {hardware} interface is proprietary. Corporations have to take care of their very own copy (fork) of an entire conventional compiler, modified to help their explicit chip. This requires hiring groups of compiler builders along with the efficiency engineers.
“In Exo, we as an alternative externalize the definition of hardware-specific backends from the exocompiler. This provides us a greater separation between Exo—which is an open-source mission—and hardware-specific code—which is usually proprietary. We have proven that we will use Exo to shortly write code that is as performant as Intel’s hand-optimized Math Kernel Library. We’re actively working with engineers and researchers at a number of corporations,” says Gilbert Bernstein, a postdoc on the College of California at Berkeley.
The way forward for Exo entails exploring a extra productive scheduling meta-language, and increasing its semantics to help parallel programming fashions to use it to much more accelerators, together with GPUs.
Ikarashi and Bernstein wrote the paper alongside Alex Reinking and Hasan Genc, each Ph.D. college students at UC Berkeley, and MIT Assistant Professor Jonathan Ragan-Kelley.

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Extra data:
Yuka Ikarashi et al, Exocompilation for productive programming of {hardware} accelerators, Proceedings of the forty third ACM SIGPLAN Worldwide Convention on Programming Language Design and Implementation (2022). DOI: 10.1145/3519939.3523446

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