Efficient layering for high speed communication: the MPI over Fast Messages (FM) experience

We describe our experience of designing, implementing, and evaluating two generations of high performance communication libraries, Fast Messages (FM) for Myrinet. In FM 1, we designed a simple interface and provided guarantees of reliable and in-order delivery, and flow control. While this was a significant improvement over previous systems, it was not enough. Layering MPI atop FM 1 showed that only about 35 % of the FM 1 bandwidth could be delivered to higher level communication APIs. Our second generation communication layer, FM 2, addresses the identified problems, providing gather-scatter, interlayer scheduling, receiver flow control, as well as some convenient API features which simplify programming. FM 2 can deliver 55–95 % to higher level APIs such as MPI. This is especially impressive as the absolute bandwidths delivered have increased over fourfold to 90 MB/s. We describe general issues encountered in matching two communication layers, and our solutions as embodied in FM 2

Characterizing Power and Energy Efficiency of Legion Data-Centric Runtime and Applications on Heterogeneous High-Performance Computing Systems

The traditional parallel programming models require programmers to explicitly specify parallelism and data movement of the underlying parallel mechanisms. Different from the traditional computation-centric programming, Legion provides a data-centric programming model for extracting parallelism and data movement. In this chapter, we aim to characterize the power and energy consumption of running HPC applications on Legion. We run benchmark applications on compute nodes equipped with both CPU and GPU, and measure the execution time, power consumption and CPU/GPU utilization. Additionally, we test the message passing interface (MPI) version of these applications and compare the performance and power consumption of high-performance computing (HPC) applications using the computation-centric and data-centric programming models. Experimental results indicate Legion applications outperforms MPI applications on both performance and energy efficiency, i.e., Legion applications can be 9.17 times as fast as MPI applications and use only 9.2% energy. Legion effectively explores the heterogeneous architecture and runs applications tasks on GPU. As far as we know, this is the first study to understand the power and energy consumption of Legion programming and runtime infrastructure. Our findings will enable HPC system designers and operators to develop and tune the performance of data-centric HPC applications with constraints on power and energy consumption

Quantum Algorithm Implementations for Beginners

As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers. While currently available quantum computers have less than 100 qubits, quantum computing hardware is widely expected to grow in terms of qubit count, quality, and connectivity. This review aims to explain the principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding of the underlying fascinating quantum mechanical principles optional. We give an introduction to quantum computing algorithms and their implementation on real quantum hardware. We survey 20 different quantum algorithms, attempting to describe each in a succinct and self-contained fashion. We show how these algorithms can be implemented on IBM's quantum computer, and in each case, we discuss the results of the implementation with respect to differences between the simulator and the actual hardware runs. This article introduces computer scientists, physicists, and engineers to quantum algorithms and provides a blueprint for their implementations