Parallelizing Games for Heterogeneous Computing Environments

Game applications nowadays run on platforms that contain multiple and different types of processors. Benefiting from the available processing power requires: (1) Code to be designed to run in parallel; and (2) Efficient mapping techniques to schedule tasks optimally on appropriate processors for increasing performance. The work in this thesis is directed towards improving the execution time performance of game applications on host platforms. The methodology adopted consisted of the following different stages: (1) Studying the main features of game applications; (2) Investigating parallelization opportunities within these applications, (3) Identifying/designing the programming structures needed to realize those opportunities; and (4) Designing, implementing, verifying and testing the parallel programming structures developed to support heterogeneous processing and maximize use of available processing power. The research reported in this thesis presents a new framework for parallel programming of games. This framework consists of two major components which form the two main contributions of our work. These are (1) Sayl, a new design pattern to enable parallel programming of game applications and to reduce the amount of programmer work when writing parallel code; and (2) Arbiter Work Stealing, a new task scheduler capable of efficiently and automatically scheduling dynamically generated tasks to processors in a heterogeneous processing and memory environment. The framework was used to successfully parallelize the serial code of a sample game application to work in a heterogeneous processing environment. The parallel version shows far superior performance as compared to the serial version

Improving the Performance of User-level Runtime Systems for Concurrent Applications

Concurrency is an essential part of many modern large-scale software systems. Applications must handle millions of simultaneous requests from millions of connected devices. Handling such a large number of concurrent requests requires runtime systems that efficiently man- age concurrency and communication among tasks in an application across multiple cores. Existing low-level programming techniques provide scalable solutions with low overhead, but require non-linear control flow. Alternative approaches to concurrent programming, such as Erlang and Go, support linear control flow by mapping multiple user-level execution entities across multiple kernel threads (M:N threading). However, these systems provide comprehensive execution environments that make it difficult to assess the performance impact of user-level runtimes in isolation. This thesis presents a nimble M:N user-level threading runtime that closes this con- ceptual gap and provides a software infrastructure to precisely study the performance impact of user-level threading. Multiple design alternatives are presented and evaluated for scheduling, I/O multiplexing, and synchronization components of the runtime. The performance of the runtime is evaluated in comparison to event-driven software, system- level threading, and other user-level threading runtimes. An experimental evaluation is conducted using benchmark programs, as well as the popular Memcached application. The user-level runtime supports high levels of concurrency without sacrificing application performance. In addition, the user-level scheduling problem is studied in the context of an existing actor runtime that maps multiple actors to multiple kernel-level threads. In particular, two locality-aware work-stealing schedulers are proposed and evaluated. It is shown that locality-aware scheduling can significantly improve the performance of a class of applications with a high level of concurrency. In general, the performance and resource utilization of large-scale concurrent applications depends on the level of concurrency that can be expressed by the programming model. This fundamental effect is studied by refining and customizing existing concurrency models

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications