Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

A Practical Hierarchial Model of Parallel Computation: The Model

We introduce a model of parallel computation that retains the ideal properties of the PRAM by using it as a sub-model, while simultaneously being more reflective of realistic parallel architectures by accounting for and providing abstract control over communication and synchronization costs. The Hierarchical PRAM (H-PRAM) model controls conceptual complexity in the face of asynchrony in two ways. First, by providing the simplifying assumption of synchronization to the design of algorithms, but allowing the algorithms to work asynchronously with each other; and organizing this control asynchrony via an implicit hierarchy relation. Second, by allowing the restriction of communication asynchrony in order to obtain determinate algorithms (thus greatly simplifying proofs of correctness). It is shown that the model is reflective of a variety of existing and proposed parallel architectures, particularly ones that can support massive parallelism. Relationships to programming languages are discussed. Since the PRAM is a sub-model, we can use PRAM algorithms as sub-algorithms in algorithms for the H-PRAM; thus results that have been established with respect to the PRAM are potentially transferable to this new model. The H-PRAM can be used as a flexible tool to investigate general degrees of locality (“neighborhoods of activity) in problems, considering communication and synchronization simultaneously. This gives the potential of obtaining algorithms that map more efficiently to architectures, and of increasing the number of processors that can efficiently be used on a problem (in comparison to a PRAM that charges for communication and synchronization). The model presents a framework in which to study the extent that general locality can be exploited in parallel computing. A companion paper demonstrates the usage of the H-PRAM via the design and analysis of various algorithms for computing the complete binary tree and the FFT/butterfly graph

Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice

We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we focus on the Adaptive Massively Parallel Computation (AMPC) model, which is a theoretical model that captures MapReduce-like computation augmented with a distributed hash table. We show the first AMPC algorithms for all of the studied problems that run in a constant number of rounds and use only $O(n^\epsilon)$ space per machine, where $0 < \epsilon < 1$. Our results improve both upon the previous results in the AMPC model, as well as the best-known results in the MPC model, which is the theoretical model underpinning many popular distributed computation frameworks, such as MapReduce, Hadoop, Beam, Pregel and Giraph. Finally, we provide an empirical comparison of the algorithms in the MPC and AMPC models in a fault-tolerant distriubted computation environment. We empirically evaluate our algorithms on a set of large real-world graphs and show that our AMPC algorithms can achieve improvements in both running time and round-complexity over optimized MPC baselines