TM2C: A software transactional memory for many-cores

Transactional memory is an appealing paradigm for concurrent programming. Many software implementations of the paradigm were proposed in the last decades for both shared memory multi-core systems and clusters of distributed machines. However, chip manufacturers have started producing many-core architectures, with low network-on-chip communication latency and limited support for cache-coherence, rendering existing transactional memory implementations inapplicable. This paper presents TM2C, the first software Transactional Memory protocol for Many-Core systems. TM2C exploits network-on-chip communications to get granted accesses to shared data through efficient message passing. In particular, it allows visible read accesses and hence effective distributed contention management with eager conflict detection. We also propose FairCM, a companion contention manager that ensures starvation-freedom, which we believe is an important property in many-core systems, as well as an implementation of elastic transactions in these settings. Our evaluation on four benchmarks, i.e., a linked list and a hash table data structures as well as a bank and a MapReduce-like applications, indicates better scalability than locks and up to 20-fold speedup (relative to bare sequential code) when running 24 application cores. © 2012 ACM

Everything You Always Wanted to Know about Synchronization but Were Afraid to Ask

This paper presents the most exhaustive study of synchronization to date. We span multiple layers, from hardware cache-coherence protocols up to high-level concurrent software. We do so on different types of architectures, from single-socket - uniform and non-uniform - to multi-socket - directory and broadcast-based - many-cores. We draw a set of observations that, roughly speaking, imply that scalability of synchronization is mainly a property of the hardware. © 2013 ACM

PGX.D/async: A scalable distributed graph pattern matching engine

Graph querying and pattern matching is becoming an important feature of graph processing as it allows data analysts to easily collect and understand information about their graphs in a way similar to SQL for databases. One of the key challenges in graph pattern matching is to process increasingly large graphs that often do not ft in the memory of a single machine. In this paper, we present PGX.D/Async, a scalable distributed pattern matching engine for property graphs that is able to handle very large datasets. PGX.D/Async implements pattern matching operations with asynchronous depth-frst traversal, allowing for a high degree of parallelism and precise control over memory consumption. In PGX.D/Async, developers can query graphs with PGQL, an SQL-like query language for property graphs. Essentially, PGX.D/Async provides an intuitive, distributed, in-memory pattern matching engine for very large graphs

PGX.D/async: A scalable distributed graph pattern matching engine

Graph querying and pattern matching is becoming an important feature of graph processing as it allows data analysts to easily collect and understand information about their graphs in a way similar to SQL for databases. One of the key challenges in graph pattern matching is to process increasingly large graphs that often do not ft in the memory of a single machine. In this paper, we present PGX.D/Async, a scalable distributed pattern matching engine for property graphs that is able to handle very large datasets. PGX.D/Async implements pattern matching operations with asynchronous depth-frst traversal, allowing for a high degree of parallelism and precise control over memory consumption. In PGX.D/Async, developers can query graphs with PGQL, an SQL-like query language for property graphs. Essentially, PGX.D/Async provides an intuitive, distributed, in-memory pattern matching engine for very large graphs