Portland State University. Department of Computer Science
Date of Award
Doctor of Philosophy (Ph.D.) in Computer Science
1 online resource (vii, 146 pages)
Concurrency, Read-copy update, Scalability, Data structures, Multicore, High performance computing, Parallel processing (Electronic computers), Functional programming (Computer science), Electronic data processing -- Distributed processing
High-performance programs and systems require concurrency to take full advantage of available hardware. However, the available concurrent programming models force a difficult choice, between simple models such as mutual exclusion that produce little to no concurrency, or complex models such as Read-Copy Update that can scale to all available resources. Simple concurrent programming models enforce atomicity and causality, and this enforcement limits concurrency. Scalable concurrent programming models expose the weakly ordered hardware memory model, requiring careful and explicit enforcement of causality to preserve correctness, as demonstrated in this dissertation through the manual construction of a scalable hash-table item-move algorithm. Recent research on "relativistic programming" aims to standardize the programming model of Read-Copy Update, but thus far these efforts have lacked a generalized memory ordering model, requiring data-structure-specific reasoning to preserve causality. I propose a new memory ordering model, "relativistic causal ordering", which combines the scalabilty of relativistic programming and Read-Copy Update with the simplicity of reader atomicity and automatic enforcement of causality. Programs written for the relativistic model translate to scalable concurrent programs for weakly-ordered hardware via a mechanical process of inserting barrier operations according to well-defined rules. To demonstrate the relativistic causal ordering model, I walk through the straightforward construction of a novel concurrent hash-table resize algorithm, including the translation of this algorithm from the relativistic model to a hardware memory model, and show through benchmarks that the resulting algorithm scales far better than those based on mutual exclusion.
Triplett, Josh, "Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures" (2012). Dissertations and Theses. Paper 497.