Janus: Statically-Driven and Profile-Guided Automatic Dynamic Binary Parallelisation

We present Janus, a framework that addresses the challenge of automatic binary parallelisation. Janus uses same-ISA dynamic binary modification to optimise application binaries, controlled by static analysis with judicious use of software speculation and runtime checks that ensure the safety of the optimisations. A static binary analyser first examines a binary executable, to determine the loops that are amenable to parallelisation and the transformations required. These are encoded as a series of rewrite rules, the steps needed to convert a serial loop into parallel form. The Janus dynamic binary modifier reads both the original executable and rewrite rules and carries out the transformations on a per-basic-block level just-in-time before execution. Lifting static analysis out of the runtime enables the global and profile-guided views of the application; ambiguities from static binary analysis can in turn be addressed through a combination of dynamic runtime checks and speculation guard against data dependence violations. It allows us to parallelise even those loops containing dynamically discovered code. We demonstrate Janus by parallelising a range of optimised SPEC CPU 2006 benchmarks, achieving average speedups of 2.1× and 6.0× in the best case.Arm Ltd Engineering and Physical Sciences Research Council (EP/K026399/1), Engineering and Physical Sciences Research Council (EP/P020011/1

Parallelizing Sequential Programs With Statistical Accuracy Tests

We present QuickStep, a novel system for parallelizing sequential programs. QuickStep deploys a set of parallelization transformations that together induce a search space of candidate parallel programs. Given a sequential program, representative inputs, and an accuracy requirement, QuickStep uses performance measurements, profiling information, and statistical accuracy tests on the outputs of candidate parallel programs to guide its search for a parallelizationthat maximizes performance while preserving acceptable accuracy. When the search completes, QuickStep produces an interactive report that summarizes the applied parallelization transformations, performance, and accuracy results for the automatically generated candidate parallel programs. In our envisioned usage scenarios, the developer examines this report to evaluate the acceptability of the final parallelization and to obtain insight into how the original sequential program responds to different parallelization strategies. Itis also possible for the developer (or even a user of the program who has no software development expertise whatsoever) to simply use the best parallelization out of the box without examining the report or further investigating the parallelization. Results from our benchmark set of applications show that QuickStep can automatically generate accurate and efficient parallel programs---the automatically generated parallel versions of five of our six benchmark applications run between 5.0 and 7.7 times faster on 8 cores than the original sequential versions. Moreover, a comparison with the Intel icc compiler highlights how QuickStep can effectively parallelize applications with features (such as the use of modern object-oriented programming constructs or desirable parallelizations with infrequent but acceptable data races) that place them inherently beyond the reach of standard approaches

Rapid Parallelization by Collaboration

The widespread adoption of Chip Multiprocessors has renewed the emphasis on the use of parallelism to improve performance. The present and growing diversity in hardware architectures and software environments, however, continues to pose difficulties in the effective use of parallelism thus delaying a quick and smooth transition to the concurrency era. In this document, we describe the research being conducted at the Computer Science Department at Columbia University on a system called COMPASS that aims to simplify this transition by providing advice to programmers considering parallelizing their code. The advice proffered to the programmer is based on the wisdom collected from programmers who have already parallelized some code. The utility of COMPASS rests, not only on its ability to collect the wisdom unintrusively but also on its ability to automatically seek, find and synthesize this wisdom into advice that is tailored to the code the user is considering parallelizing and to the environment in which the optimized program will execute in. COMPASS provides a platform and an extensible framework for sharing human expertise about code parallelization -- widely and on diverse hardware and software. By leveraging the "Wisdom of Crowds" model which has been conjunctured to scale exponentially and which has successfully worked for Wikis, COMPASS aims to enable rapid parallelization of code and thus continue to extend the benefits for Moore's law scaling to science and society

Advancements in Compiler Design and Optimization Techniques

The modern period has seen advancements in compiler design, optimization technique, and software system efficiency. The influence of the most recent developments in compiler design and optimization techniques on program execution speed, memory utilization, and overall software quality is highlighted in this study. The design of the compiler is advanced by the efficient code that is now structured in research with high-speed performance without manual intervention. The influence of the most recent developments in compiler design and optimization techniques on program execution speed, memory utilization, and overall software quality is highlighted in this paper's thorough analysis