Software Solutions Aim to Accelerate AI, Quantum, and GPU Processing

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With the goal of accelerating various types of advanced computing, including AI, GPU, and quantum processing, a variety of software solutions have recently been unveiled.

www.allaboutcircuits.com, Aug. 28, 2022 – 

A crop of novel software innovations have emerged from leading processor vendors and academia. These solutions promise significant improvements to various applications, including artificial intelligence (AI), high-performance computing (HCP), health care, manufacturing, and more.

These innovations offer several benefits to system developers and EEs, especially in bridging the gap within software-hardware integration and accelerating AI, quantum processing, and GPU systems.

In this article, we'll provide an overview of these innovations, exploring their impact on modern-day computing.

Programming Hardware Accelerators with Exocompilation

In a recently published paper, MIT researcher Yuka Ikarashi, and his team of researchers from MIT and UC Berkeley, developed and demonstrated the capability of a new programming language that enables the high-performance coding of hardware accelerators.

Called Exo, this new programming language dramatically minimizes the workload of engineers and developers tasked with configuring out-of-box hardware accelerators with software to optimize compatibility with entire application systems. Moreover, Exo allows low-level performance engineers to achieve complex program transformation from simple programs at unprecedented speeds using special accelerator chips.

The language can leverage these accelerators in a process called "Exocompilation" to convert a simple matrix multiplication into a complex program and run it at high speeds.

Exocompilation Prioritizes Performance

Unlike existing studies that aim at automating the optimization process for relevant hardware, which, in turn, leads to little to zero process improvements for performance engineers, exocompilation ensures performance engineers prioritize system performance improvements over complex code debugging and optimizations.

With Exo, engineers can isolate and externalize hardware-specific backends (or optimizations) from the compiler and achieve system efficiencies up to 90%. The externalization better separates the open-source project Exo programming language and the proprietary hardware-specific code.

Although Exo leverages specific hardware accelerator chips, the team identified the possibility of expanding its semantics to support parallel programming models and to apply it to even more accelerators, including graphics processing units (GPUs).

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