Multicore processors have become prevalent and spurred interest in scalable synchronization mechanisms, such as Read-Copy Update. While RCU is popular in monolithic operating system kernels it has yet to see an implementation in a microkernel environment. In this thesis we design and implement RCU for the microkernel operating system HelenOS. Moreover, we explore potential uses of RCU in HelenOS and illustrate its utility in both the kernel and user space. Benchmarks demonstrate that the RCU implementation provides linearly scalable read-sides and incurs significantly less overhead than traditional locking even if uncontended. Furthermore, RCU was used in user space to speed up traditional locking 2.6 times in the common case. In the kernel, RCU ensured linear scalability of a previously non-scalable futex subsystem. Powered by TCPDF (www.tcpdf.org)

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.