Toward Linearizability Testing for Multi-Word Persistent Synchronization Primitives

Persistent memory makes it possible to recover in-memory data structures following a failure instead of rebuilding them from state saved in slow secondary storage. Implementing such recoverable data structures correctly is challenging as their underlying algorithms must deal with both parallelism and failures, which makes them especially susceptible to programming errors. Traditional proofs of correctness should therefore be combined with other methods, such as model checking or software testing, to minimize the likelihood of uncaught defects. This research focuses specifically on the algorithmic principles of software testing, particularly linearizability analysis, for multi-word persistent synchronization primitives such as conditional swap operations. We describe an efficient decision procedure for linearizability in this context, and discuss its practical applications in detecting previously-unknown bugs in implementations of multi-word persistent primitives

Landscapes of data sets and functoriality of persistent homology

The aim of this article is to describe a new perspective on functoriality of persistent homology and explain its intrinsic symmetry that is often overlooked. A data set for us is a finite collection of functions, called measurements, with a finite domain. Such a data set might contain internal symmetries which are effectively captured by the action of a set of the domain endomorphisms. Different choices of the set of endomorphisms encode different symmetries of the data set. We describe various category structures on such enriched data sets and prove some of their properties such as decompositions and morphism formations. We also describe a data structure, based on coloured directed graphs, which is convenient to encode the mentioned enrichment. We show that persistent homology preserves only some aspects of these landscapes of enriched data sets however not all. In other words persistent homology is not a functor on the entire category of enriched data sets. Nevertheless we show that persistent homology is functorial locally. We use the concept of equivariant operators to capture some of the information missed by persistent homology

Persistent Data Structures for Incremental Join Indices

Join indices are used in relational databases to make join operations faster. Join indices essentially materialise the results of join operations and so accrue maintenance cost, which makes them more suitable for use cases where modifications are rare and joins are performed frequently. To make the maintenance cost lower incrementally updating existing indices is to be preferred. The usage of persistent data structures for the join indices were explored. Motivation for this research was the ability of persistent data structures to construct multiple partially different versions of the same data structure memory efficiently. This is useful, because there can exist different versions of join indices simultaneously due to usage of multi-version concurrency control (MVCC) in a database. The techniques used in Relaxed Radix Balanced Trees (RRB-Trees) persistent data structure were found promising, but none of the popular implementations were found directly suitable for the use case. This exploration was done from the context of a particular proprietary embedded in-memory columnar multidimensional database called FastormDB developed by RELEX Solutions. This focused the research into Java Virtual Machine (JVM) based data structures as the implementation of FastormDB is in Java. Multiple persistent data-structures made for the thesis and ones from Scala, Clojure and Paguro were evaluated with Java Microbenchmark Harness (JMH) and Java Object Layout (JOL) based benchmarks and their results analysed via visualisations

Fine-Grain Checkpointing with In-Cache-Line Logging

Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cost of ensuring the order in which memory writes reach NVM. Often, this requires flushing data to NVM and waiting a full memory round-trip time. In this paper, we introduce two new techniques: Fine-Grained Checkpointing, which ensures a consistent, quickly recoverable data structure in NVM after a system failure, and In-Cache-Line Logging, an undo-logging technique that enables recovery of earlier state without requiring cache-line flushes in the normal case. We implemented these techniques in the Masstree data structure, making it persistent and demonstrating the ease of applying them to a highly optimized system and their low (5.9-15.4\%) runtime overhead cost.Comment: In 2019 Architectural Support for Programming Languages and Operating Systems (ASPLOS 19), April 13, 2019, Providence, RI, US