Communication Performance Models Research Articles

C-Lop: Accurate contention-based modeling of MPI concurrent communication

MPI communication optimization is a crucial stage to optimize high-performance applications. As a formal analysis of MPI communication, the communication performance models have made some achievements in improving the efficiency of collective algorithms and optimizing communication scheduling. However, previous models are difficult to model asynchronous concurrent communication and do not take into account numerous contention factors. In this paper, we present C-Lop, an incremental MPI performance model based on τ-Lop. Firstly, C-Lop proposes a method for asynchronous modeling of concurrent communication. As the only model that considers concurrent transmission, τ-Lop describes the cost of the all processes as a whole without distinguishing the cost of each process. Here, C-Lop uses the idea of asynchronous modeling that describe the cost of the system by averaging the communication cost per process. It can describe the communication cost for some systems with out-of-sync communication more accurately. Moreover, C-Lop introduces the parameter C to represent the contention, and considers the contention of concurrent transmissions on network-on-chip, data reuse, and contention of noncommunication processes to make a more accuracy estimation. Furthermore, parameter C can be customized to fit more application scenarios. In addition, we evaluate several common collective algorithms, a matrix multiplication algorithm (SUMMA), and two kinds of communication in a three-dimensional multi-grid application on the Tianhe-3 prototype, and results show that C-Lop outperforms the competition.

LogSC: Model-based one-sided communication performance estimation

One-sided communication (also known as remote memory access, or RMA) in the Message Passing Interface (MPI) is a communication interface that has been introduced in MPI-2 (1997) that enables new more efficient programming models. In MPI-3, some new more flexible and efficient primitives have been introduced, which makes it easier to use and more deployable.However, compared to traditional two-sided communication, little work has been performed on analyzing one-sided communication costs, which urgently requires formal analysis. The communication performance model is a formal analysis of communication and its cost. We focus on the software model, whose core idea is that the transmission can be represented as a sequence of implicit transfers and data movements. This approach is a suitable solution for concurrent communication modeling.We propose LogSC, which consists of the window cost, transmission cost, synchronization cost, and computational cost in atomic operations. In this paper, LogSC is used to model most of the operations in one-sided communication, including the put/get operation, atomic operation, and shared memory programming of MPI. We model and evaluate the parallel tests of IMB, collectives designed by combining MPI and the MPI shared memory (MPI+MPI), and the communication in the scalable universal matrix multiplication algorithm (SUMMA), which is a common matrix multiplication algorithm. Experiments show that our modeling has high accuracy, which makes up for the lack of existing models.

A tool to assess the communication cost of parallel kernels on heterogeneous platforms

Ensuring applications to achieve an efficient usage of resources and fast execution time in the complex current heterogeneous high-performance computing platforms is a paramount problem. Essential efforts to reach the goal are the optimal partitioning of the data space between the processes composing a typical task/data-parallel application, and their right mapping and deployment on the platform. The computational and communication performance modeling describing the platform and the application behaviors is an increasingly recognized approach. This paper discusses the utility of the $$\uptau$$–Lop analytic communication performance model in facing these issues and contributes with a practical symbolic computation tool that represents, manipulates and accurately evaluates the formal communication cost expression derived from a hybrid kernel. We identify a set of scenarios where the tool could be applied, provide with both basic and advanced use examples and evaluate the tool on real-life kernels.

Performance Optimization and Modeling of Fine-Grained Irregular Communication in UPC

The Unified Parallel C (UPC) programming language offers parallelism via logically partitioned shared memory, which typically spans physically disjoint memory subsystems. One convenient feature of UPC is its ability to automatically execute between-thread data movement, such that the entire content of a shared data array appears to be freely accessible by all the threads. The programmer friendliness, however, can come at the cost of substantial performance penalties. This is especially true when indirectly indexing the elements of a shared array, for which the induced between-thread data communication can be irregular and have a fine-grained pattern. In this paper, we study performance enhancement strategies specifically targeting such fine-grained irregular communication in UPC. Starting from explicit thread privatization, continuing with block-wise communication, and arriving at message condensing and consolidation, we obtained considerable performance improvement of UPC programs that originally require fine-grained irregular communication. Besides the performance enhancement strategies, the main contribution of the present paper is to propose performance models for the different scenarios, in the form of quantifiable formulas that hinge on the actual volumes of various data movements plus a small number of easily obtainable hardware characteristic parameters. These performance models help to verify the enhancements obtained, while also providing insightful predictions of similar parallel implementations, not limited to UPC, that also involve between-thread or between-process irregular communication. As a further validation, we also apply our performance modeling methodology and hardware characteristic parameters to an existing UPC code for solving a 2D heat equation on a uniform mesh.

A Survey of Communication Performance Models for High-Performance Computing

This survey aims to present the state of the art in analytic communication performance models, providing sufficiently detailed descriptions of particularly noteworthy efforts. Modeling the cost of communications in computer clusters is an important and challenging problem. It provides insights into the design of the communication pattern of parallel scientific applications and mathematical kernels and sets a clear ground for optimization of their deployment in the increasingly complex high-performance computing infrastructure. The survey provides background information on how different performance models represent the underlying platform and shows the evolution of these models over time from early clusters of single-core processors to present-day multi-core and heterogeneous platforms. Prospective directions for future research in the area of analytic communication performance modeling conclude the survey.

Analytical Communication Performance Models as a metric in the partitioning of data-parallel kernels on heterogeneous platforms

Data partitioning on heterogeneous HPC platforms is formulated as an optimization problem. The algorithm departs from the communication performance models of the processes representing their speeds and outputs a data tiling that minimizes the communication cost. Traditionally, communication volume is the metric used to guide the partitioning, but such metric is unable to capture the complexities introduced by uneven communication channels and the variety of patterns in the kernel communications. We discuss Analytical Communication Performance Models as a new metric in partitioning algorithms. They have not been considered in the past because of two reasons: prediction inaccuracy and lack of tools to automatically build and solve kernel communication formal expressions. We show how communication performance models fit the specific kernel and platform, and we present results that equal or even improve previous volume-based strategies.

Model-Based Estimation of the Communication Cost of Hybrid Data-Parallel Applications on Heterogeneous Clusters

Heterogeneous systems composed of CPUs and accelerators sharing communication channels of different performance are getting mainstream in HPC but, at the same time, they show a complexity that makes it difficult to optimize the deployment of a data parallel application. Recent analytical tools such as Functional Performance Models, combined with advanced partitioning algorithms, manage to achieve a balanced configuration by distributing the workload unevenly, according to the performance of the different processing units. Unfortunately, such uneven distribution of the computation load leads to communication unbalances that, very often, render worthless the previous workload balancing efforts. Finding the optimal communication scheme without expensive testing on the executing platform requires an analytical approach to the estimation of the communication cost of different configurations of the application. With this goal in mind, we propose and discuss an extension of the $\tau$ -Lop communication performance model to cover heterogeneous architectures. In order to provide a quantitative assessment of this extended model, we conduct experiments with two representative computational kernels, the SUMMA algorithm and the 2D wave equation solver. The $\tau$ -Lop predictions are compared against the HLogGP model and the observed costs for a variety of configurations, hardware resources and problem sizes.

Research on MAC layer communication performance model of wireless sensor networks

Research on MAC layer communication performance model of wireless sensor networks

Accurate Heterogeneous Communication Models and a Software Tool for Their Efficient Estimation

In this paper, we analyze the restrictions of traditional communication performance models that affect the accuracy of analytical prediction of the execution time of collective communication operations on homogeneous and heterogeneous clusters. In particular, we show that the constant and variable contributions of processors and the network are not fully separated in these models. Full separation of the contributions that have different natures and arise from different sources would lead to more intuitive and accurate models, but the parameters of such models cannot be estimated from only the point-to-point experiments, which are usually used for traditional models. The paper presents such an intuitive and accurate point-to-point model and describes a set of communication experiments sufficient for estimation of its parameters. It also presents an implementation of the new model in the form of a software tool that automates the estimation of both this model and heterogeneous extensions of traditional communication performance models. We conclude with a presentation of experimental results demonstrating that the elaborated model much more accurately predicts the execution time of different algorithms of collective operations than traditional models.

Accurate and Efficient Estimation of Parameters of Heterogeneous Communication Performance Models

Analytical predictive communication models play an important role in the optimization of communication operations in scientific applications running on computational clusters. The effectiveness of this model-based optimization strongly depends on the accuracy of the estimation of the parameters of these models. The task of accurate estimation of the model is particularly challenging for heterogeneous communication models that use a much larger number of point-to-point parameters than their homogeneous counterparts. One particular challenge occurs when the number of point-to-point parameters describing communication between a pair of processors becomes larger than the number of independent point-to-point communication experiments traditionally used for estimation of the parameters. In this paper, we address this and other related issues and propose an approach that allows us to design a set of communication experiments sufficient for the accurate and efficient estimation of the parameters of a heterogeneous communication performance model. The experiments on heterogeneous clusters demonstrate the accuracy and efficiency of the proposed solution.

Performance modeling of communication and computation in hybrid MPI and OpenMP applications

Performance evaluation and modeling are crucial steps to enabling the optimization of parallel programs. Programs written using two programming models, such as MPI and OpenMP, require analysis to determine both performance efficiency and the most suitable numbers of processes and threads for their execution on a given platform. To study both of these problems, we propose the construction of a model that is based upon a small number of parameters, but is able to capture the complexity of the runtime system. We incorporate measurements of overheads introduced by each of the programming models, and thus need to model both the network and computational aspects of the system. We have combined two different techniques that includes static analysis, driven by the OpenUH compiler, to retrieve application signatures and a parallelization overhead measurement benchmark, realized by Sphinx and Perfsuite, to collect system profiles. Finally, we propose a performance evaluation measurement to identify communication and computation efficiency. In this paper, we describe our underlying framework, the performance model, and show how our tool can be applied to a sample code.

A detailed MPI communication model for distributed systems

Message Passing Interface (MPI) is the most popular communication interface used in today PC clusters and other cluster-type parallel/distributed computers. Up-to-date the most popular analytical MPI communication performance model for parallel/distributed machines is the LogGP model, which is mostly based on system hardware parameters. Due to the popularity of MPI, the improvements of connection network in the past few years, and the development of MPI for computational Grids, LogGP model needs to be re-evaluated for the detailed hardware performance and for the inclusion of middleware overheads for different data structures. In this article, we use our experiment results to show that the current LogGP communication model is too limited for today parallel/distributed systems. We propose a modification by including into the model important factors that have been left out. We itemize the terms of the model to show the consistency and the meaning of these communication costs, which we believe to be the starting point for modeling MPI communication cost on the Grids. In this work, we start with point-to-point communication and plan to extend to other communication patterns, such as the broadcast communication.

Enhancing Mobile Agent Communication Performance

The recent evolution of distributed systems has favoured the development of a new generation of mobile agents based applications. In fact, many agent systems have been created in the last few years, and there are currently various efforts to standardize them. However, some problems are still under research, including mechanisms for agent security, control structures, transactional support, and design of communication models. Previous research [1] has shown that better communication performance model can be achieved by mixing remote procedure calls with agent migration. In this article we propose an algorithm to provide the agent with a path with minimal communication cost for some special network topologies.

Integrating TwoTowers and GreatSPN through a compact net semantics

Stochastic process algebras (SPAs) and stochastic Petri nets (SPNs) are two well known formal methods for the functional and performance modeling and analysis of computer, communication and software systems. Starting from the mappings from process algebras to Petri nets proposed in the literature to provide a truly concurrent semantic framework to concurrent programming languages, in this paper, we define a new SPN semantics for SPAs in order to facilitate the integration and the cross-fertilization between the two formalisms. We then prove that our net semantics is correct via a retrievability result. Afterwards, we demonstrate that it improves on the previously proposed net semantics with respect to the size of the resulting SPNs and on the standard interleaving semantics because of the detection of system symmetries. Furthermore, we illustrate its usefulness by showing how to reinterpret at the SPA level the results efficiently obtainable at the SPN level. Finally, we describe the implementation of our net semantics that has been realized to integrate the extended Markovian process algebra with generative–reactive synchronizations (EMPA gr) based software tool TwoTowers with the generalized stochastic Petri net based software tool GreatSPN.

Usability of communication performance models: A video-on-demand case study

Communication performance models enable networked multimedia applications to describe their performance requirements as regards throughput, delay, jitter, and loss behaviour of a particular communication service. This paper presents four recently proposed models and demonstrates their usability with a video-on-demand example. The selected models are two models designed by the Tenet Group, one model on which ATM channel traffic is characterized by the ATM Forum, and one model published as RFC1363 by the Internet community.