Today's cutting-edge applications, ranging from wearable devices and embedded medical sensors to high-performance data centers, put new demands on computer architectures. Those demands include more computation capability, a tight power budget, low latency, high throughput, and many more. To meet these requirements, specialized architectures with low energy consumption are becoming more prevalent. Many of these architectures trade off programmability features for gains in energy efficiency and performance. Hence, programmability challenges are inevitable as applications continue to evolve and make new demands on computing architectures.
I propose key principles for improving programmability intended for application writers as well as compiler developers and language designers. First, I address programmability issues by providing a programming model that hides low-level details but sufficiently exposes essential details for application writers to control. Second, to compile and optimize programs, I apply a new compilation methodology based on synthesis. Unlike a classical compiler's transformation, synthesis obtains a correct and optimal solution by searching for an optimal candidate that is semantically equivalent to a specification program. This search helps compilers generate efficient code without deriving a program via a sequence of transformations, which are challenging for compiler developers to design for new unconventional architectures.
In this thesis, I demonstrate the key principles in three projects: Chlorophyll, a language and compiler for low-power spatial architectures; Floem, a programming system for NIC-accelerated data center applications; and GreenThumb, a framework for building a superoptimizer (an assembly program optimizer based on synthesis).
Programming Abstractions and Synthesis-Aided Compilation for Emerging Computing Platforms
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