Existing iterative compilation and machine learning-based optimization techniques have been proven very successful in achieving better optimizations than the standard optimization levels of a compiler. However, they were not engineered to support the tuning of a compiler’s optimizer as part of the compiler’s daily development cycle. In this paper, we first establish the required properties that a technique must exhibit to enable such tuning. We then introduce an enhancement to the classic nightly routine testing of compilers, which exhibits all the required properties and thus is capable of driving the improvement and tuning of the compiler’s common optimizer. This is achieved by leveraging resource usage and compilation information collected while systematically exploiting prefixes of the transformations applied at standard optimization levels. Experimental evaluation using the LLVM v6.0.1 compiler demonstrated that the new approach was able to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U and the Arm Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+. As a case study, we demonstrate how the insights from our approach enabled us to identify and remove a significant shortcoming of the CFG simplification pass of the LLVM v6.0.1 compiler.
Projectagami is a foldable mobile device that leverages simple 2D origami form to give applications new affordances: enabling rapid customization of the device shape. This bridges the gap between differently sized touch screen devices, and enables a novel and mobile form of split screen touch interaction that deviates from traditional rectangular form. We show how a flexible mobile device with unrestricted folding can create more realistic and augmented experiences: building on people's existing abilities to manipulate real world objects. We demonstrate this with shape enhanced "Wizard of Oz" demo applications including a book, online shopping, a board game, street navigation, and a dynamic browser. Our prototype is tracked with a Kinect and uses a projector for visual output. In the future, we envision devices like Projectagami to be selfcontained and extended to 3D form.
Garbage collected programming languages, such as Python and C#, have accelerated software development. These modern languages increase productivity and software reliability as they provide high-level data representation and control structures. Modern languages are widely used in software development for mobile, desktop, and server devices, but their adoption is limited in real-time embedded systems. There is clear interest in supporting modern languages in embedded devices as emerging markets, like the Internet of Things, demand ever smarter and more reliable products. Multiple commercial and open-source projects, such as Zerynth and MicroPython, are attempting to provide support. But these projects rely on software garbage collectors that impose high overheads and introduce unpredictable pauses, preventing their use in many embedded applications. These limitations arise from the unsuitability of conventional processors for performing efficient, predictable garbage collection. We propose the Integrated Hardware Garbage Collector (IHGC); a garbage collector tightly coupled with the processor that runs continuously in the background. Further, we introduce a static analysis technique to guarantee that real-time programs are never paused by the collector. Our design allocates a memory cycle to the collector when the processor is not using the memory. The IHGC achieves this by careful division of collection work into single-memory-access steps that are interleaved with the processor’s memory accesses. As a result, our collector eliminates run-time overheads and enables real-time program analysis. The principles behind the IHGC can be used in conjunction with existing architectures. For example, we simulated the IHGC alongside the ARMv6-M architecture. Compared to a conventional processor, our experiments indicate that the IHGC offers 1.5–7 times better performance for programs that rely on garbage collection. The IHGC delivers the benefits of garbage-collected languages with real-time performance but without the complexity and overheads inherent in software collectors.
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