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You searched for id:"oai:etd.ohiolink.edu:osu149269601946527". One record found.

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1. Sengupta, Aritra. Efficient Compiler and Runtime Support for Serializability and Strong Semantics on Commodity Hardware.

Degree: PhD, Computer Science and Engineering, 2017, The Ohio State University

Parallel software systems often have non-deterministic behavior and suffer from reliability issues. A major deterrent to the use of sound and precise bug-detection techniques is the impractical run-time overhead and poor scalability of such analyses. Alternatively, strong memory models or program execution models could achieve software reliability. Unfortunately, existing analyses and systems that provide stronger guarantees in ill-synchronized programs are either expensive or they compromise on reliability for performance. Building efficient runtime support for enforcing strong program semantics remains a challenging problem because of the overhead incurred to implement the complex mechanisms in the underlying system. The overhead could be because of restriction on reordering of operations in the compiler or the hardware, instrumentation cost in enforcing stronger program semantics through a dynamic analysis, the cost of concurrency control or synchronization mechanisms typically required in such an analysis, and the cost of leveraging specific hardware constructs. Researchers have proposed strong memory models based on serializability of regions of code—where regions of code across threads execute in isolation with each other. The run-time cost incurred to enforce strong memory models based on serializability stems from the unbounded number of operations that are monitored by an analysis. This thesis solves this critical problem of providing region serializability at reasonable overheads and yet demonstrating its efficacy in eliminating erroneous behavior from ill-synchronized programs. Providing serializability of code regions using compiler techniques and dynamic analysis while leveraging both generic and specialized commodity hardware, at low overheads, across concurrent programs having diverse characteristics—can make enforcement of strong semantics for real-world programs practical and scalable. This work presents a memory model based on bounded regions, called dynamically bounded region serializability (DBRS) and establishes the memory model theoretically—contrasting it with other memory models. It provides a novel technique, called EnfoRSer, that enforces the memory model practically, leveraging not only dynamically bounded, but statically bounded, intra-procedural, acyclic regions of code. This technique utilizes a lightweight conflict-detection mechanism and strong compiler transformations to enforce DBRS at low overheads. Further, an additional dynamic analysis based on prior work demonstrates empirically DBRS’s potential to eliminate real-world bugs. After establishing the benefits of DBRS, this thesis provides a mechanism to reduce the instrumentation overhead of DBRS enforcement by proposing a novel technique that efficiently hybridizes per-access locks and per-region locks based on the results of static data race detection, offline profiling, and a cost-benefit model. This technique demonstrates that for programs with low inter-thread dependences and high density of memory accesses, a hybridized… Advisors/Committee Members: Bond, Michael (Advisor).

Subjects/Keywords: Computer Engineering; concurrency; memory models; data races; strong semantics; compiler transformations; static analysis; dynamic analysis; hardware transactional memory; code generation; performance optimization

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APA (6th Edition):

Sengupta, A. (2017). Efficient Compiler and Runtime Support for Serializability and Strong Semantics on Commodity Hardware. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu149269601946527

Chicago Manual of Style (16th Edition):

Sengupta, Aritra. “Efficient Compiler and Runtime Support for Serializability and Strong Semantics on Commodity Hardware.” 2017. Doctoral Dissertation, The Ohio State University. Accessed September 22, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu149269601946527.

MLA Handbook (7th Edition):

Sengupta, Aritra. “Efficient Compiler and Runtime Support for Serializability and Strong Semantics on Commodity Hardware.” 2017. Web. 22 Sep 2017.

Vancouver:

Sengupta A. Efficient Compiler and Runtime Support for Serializability and Strong Semantics on Commodity Hardware. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2017 Sep 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu149269601946527.

Council of Science Editors:

Sengupta A. Efficient Compiler and Runtime Support for Serializability and Strong Semantics on Commodity Hardware. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu149269601946527

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