We argue that there is virtually no practical situation in which one should seek a "theoretically wait-free" algorithm at the expense of a state-of-the-art blocking algorithm in the case of search data structures: blocking algorithms are simple, fast, and can be made "practically wait-free". We draw this conclusion based on the most exhaustive study of blocking search data structures to date. We consider (a) different search data structures of different sizes, (b) numerous uniform and non-uniform workloads, representative of a wide range of practical scenarios, with different percentages of update operations, (c) with and without delayed threads, (d) on different hardware technologies, including processors providing HTM instructions. We explain our claim that blocking search data structures are practically wait-free through an analogy with the birthday paradox, revealing that, in state-of-the-art algorithms implementing such data structures, the probability of conflicts is extremely small. When conflicts occur as a result of context switches and interrupts, we show that HTM-based locks enable blocking algorithms to cope with them.

We introduce "asynchronized concurrency (ASCY)," a paradigm consisting of four complementary programming patterns. ASCY calls for the design of concurrent search data structures (CSDSs) to resemble that of their sequential counterparts. We argue that ASCY leads to implementations which are portably scalable: they scale across different types of hardware platforms, including single and multi-socket ones, for various classes of workloads, such as read-only and read-write, and according to different performance metrics, including throughput, latency, and energy. We substantiate our thesis through the most exhaustive evaluation of CSDSs to date, involving 6 platforms, 22 state-of-the-art CSDS algorithms, 10 re-engineered state-of-the-art CSDS algorithms following the ASCY patterns, and 2 new CSDS algorithms designed with ASCY in mind. We observe up to 30% improvements in throughput in the re-engineered algorithms, while our new algorithms out-perform the state-of-the-art alternatives.

Scaling to a large number of cores with non-uniform communication latency and unpredictable response time may call for viewing a modern many-core architecture as a distributed system. In this view, the cores replicate shared data and ensure consistency among replicas through a message-passing based agreement protocol.

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.

SUMMARY In this paper, we consider the problem of scheduling streaming applications described by complex task graphs on a heterogeneous multicore platform, the IBM QS 22 platform, embedding two STI Cell Broadband Engine processor. We first derive a complete computation and communication model of the platform on the basis of comprehensive benchmarks. Then we use this model to express the problem of maximizing the throughput of a streaming application on this platform. Although the problem is proven NP‐complete, we present an optimal solution based on mixed linear programming. We also propose simpler scheduling heuristics to compute mapping of the application task graph on the platform. We then come back to the platform and propose a scheduling software to deploy streaming applications on this platform. This allows us to thoroughly test our scheduling strategies on the real platform. We thus show that we are able to achieve a good speed‐up either with the mixed linear programming solution or using involved scheduling heuristics. Copyright © 2011 John Wiley & Sons, Ltd.

SUMMARY The clustering and sorting behavior of ants, as well as the foraging behavior of birds in nature represented sources of inspiration for designing clustering methods applicable in computer science. This paper investigates how biologically‐inspired clustering methods can be adapted to cluster Semantic Web services aiming at the efficiency of the discovery process. The methods consider the semantic similarity between services as the main clustering criterion. To measure the semantic similarity between two services, we propose a matching method that evaluates the degree of match between the semantic description of the two services. We have tested the biologically‐inspired clustering methods on the SAWSDL service retrieval test collection (SAWSDL‐TC) benchmark, and we have comparatively evaluated their performance using the Dunn index and the Average‐Item Cluster Similarity metric, the latter being introduced in this paper. Copyright © 2011 John Wiley & Sons, Ltd.