Data dependent energy modelling for worst case energy consumption analysis

Safely meeting Worst Case Energy Consumption (WCEC) criteria requires accurate energy modeling of software. We investigate the impact of instruction operand values upon energy consumption in cacheless embedded processors. Existing instruction-level energy models typically use measurements from random input data, providing estimates unsuitable for safe WCEC analysis. We examine probabilistic energy distributions of instructions and propose a model for composing instruction sequences using distributions, enabling WCEC analysis on program basic blocks. The worst case is predicted with statistical analysis. Further, we verify that the energy of embedded benchmarks can be characterised as a distribution, and compare our proposed technique with other methods of estimating energy consumption

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

Enabling Program Analysis Through Deterministic Replay and Optimistic Hybrid Analysis

As software continues to evolve, software systems increase in complexity. With software systems composed of many distinct but interacting components, today’s system programmers, users, and administrators find themselves requiring automated ways to find, understand, and handle system mis-behavior. Recent information breaches such as the Equifax breach of 2017, and the Heartbleed vulnerability of 2014 show the need to understand and debug prior states of computer systems. In this thesis I focus on enabling practical entire-system retroactive analysis, allowing programmers, users, and system administrators to diagnose and understand the impact of these devastating mishaps. I focus primarly on two techniques. First, I discuss a novel deterministic record and replay system which enables fast, practical recollection of entire systems of computer state. Second, I discuss optimistic hybrid analysis, a novel optimization method capable of dramatically accelerating retroactive program analysis. Record and replay systems greatly aid in solving a variety of problems, such as fault tolerance, forensic analysis, and information providence. These solutions, however, assume ubiquitous recording of any application which may have a problem. Current record and replay systems are forced to trade-off between disk space and replay speed. This trade-off has historically made it impractical to both record and replay large histories of system level computation. I present Arnold, a novel record and replay system which efficiently records years of computation on a commodity hard-drive, and can efficiently replay any recorded information. Arnold combines caching with a unique process-group granularity of recording to produce both small, and quickly recalled recordings. My experiments show that under a desktop workload, Arnold could store 4 years of computation on a commodity 4TB hard drive. Dynamic analysis is used to retroactively identify and address many forms of system mis-behaviors including: programming errors, data-races, private information leakage, and memory errors. Unfortunately, the runtime overhead of dynamic analysis has precluded its adoption in many instances. I present a new dynamic analysis methodology called optimistic hybrid analysis (OHA). OHA uses knowledge of the past to predict program behaviors in the future. These predictions, or likely invariants are speculatively assumed true by a static analysis. This creates a static analysis which can be far more accurate than its traditional counterpart. Once this predicated static analysis is created, it is speculatively used to optimize a final dynamic analysis, creating a far more efficient dynamic analysis than otherwise possible. I demonstrate the effectiveness of OHA by creating an optimistic hybrid backward slicer, OptSlice, and optimistic data-race detector OptFT. OptSlice and OptFT are just as accurate as their traditional hybrid counterparts, but run on average 8.3x and 1.6x faster respectively. In this thesis I demonstrate that Arnold’s ability to record and replay entire computer systems, combined with optimistic hybrid analysis’s ability to quickly analyze prior computation, enable a practical and useful entire system retroactive analysis that has been previously unrealized.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144052/1/ddevec_1.pd