Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms

International audienceThe study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments for arbitrary hypothetical scenarios. Two key concerns are accuracy (so that simulation results are scientifically sound) and scalability (so that simulation experiments can be fast and memory-efficient). While the scalability of a simulator is easily measured, the accuracy of many state-of-the-art simulators is largely unknown because they have not been sufficiently validated. In this work we describe recent accuracy and scalability advances made in the context of the SimGrid simulation framework. A design goal of SimGrid is that it should be versatile, i.e., applicable across all aforementioned domains. We present quantitative results that show that SimGrid compares favorably to state-of-the-art domain-specific simulators in terms of scalability, accuracy, or the trade-off between the two. An important implication is that, contrary to popular wisdom, striving for versatility in a simulator is not an impediment but instead is conducive to improving both accuracy and scalability

SimGrid: a Sustained Effort for the Versatile Simulation of Large Scale Distributed Systems

In this paper we present Simgrid, a toolkit for the versatile simulation of large scale distributed systems, whose development effort has been sustained for the last fifteen years. Over this time period SimGrid has evolved from a one-laboratory project in the U.S. into a scientific instrument developed by an international collaboration. The keys to making this evolution possible have been securing of funding, improving the quality of the software, and increasing the user base. In this paper we describe how we have been able to make advances on all three fronts, on which we plan to intensify our efforts over the upcoming years.Comment: 4 pages, submission to WSSSPE'1

Platform independent profiling of a QCD code

The supercomputing platforms available for high performance computing based research evolve at a great rate. However, this rapid development of novel technologies requires constant adaptations and optimizations of the existing codes for each new machine architecture. In such context, minimizing time of efficiently porting the code on a new platform is of crucial importance. A possible solution for this common challenge is to use simulations of the application that can assist in detecting performance bottlenecks. Due to prohibitive costs of classical cycle-accurate simulators, coarse-grain simulations are more suitable for large parallel and distributed systems. We present a procedure of implementing the profiling for openQCD code [1] through simulation, which will enable the global reduction of the cost of profiling and optimizing this code commonly used in the lattice QCD community. Our approach is based on well-known SimGrid simulator [2], which allows for fast and accurate performance predictions of HPC codes. Additionally, accurate estimations of the program behavior on some future machines, not yet accessible to us, are anticipated

Toward More Scalable Off-Line Simulations of MPI Applications

International audienceThe off-line (or post-mortem) analysis of execution event traces is a popular approach to understand the performance of HPC applications that use the message passing paradigm. Combining this analysis with simulation makes it possible to " replay " the application execution to explore " what if? " scenarios, e.g., assessing application performance in a range of (hypothetical) execution environments. However, such off-line analysis faces scalability issues for acquiring, storing, or replaying large event traces. We first present two previously proposed and complementary frameworks for off-line replaying of MPI application event traces, each with its own objectives and limitations. We then describe how these frameworks can be combined so as to capitalize on their respective strengths while alleviating several of their limitations. We claim that the combined framework affords levels of scalability that are beyond that achievable by either one of the two individual frameworks. We evaluate this framework to illustrate the benefits of the proposed combination for a more scalable off-line analysis of MPI applications

SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App

Numerical simulations of fluids in astrophysics and computational fluid dynamics (CFD) are among the most computationally-demanding calculations, in terms of sustained floating-point operations per second, or FLOP/s. It is expected that these numerical simulations will significantly benefit from the future Exascale computing infrastructures, that will perform 10^18 FLOP/s. The performance of the SPH codes is, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. In this work an extensive study of three SPH implementations SPHYNX, ChaNGa, and XXX is performed, to gain insights and to expose any limitations and characteristics of the codes. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. We implemented a rotating square patch as a joint test simulation for the three SPH codes and analyzed their performance on a modern HPC system, Piz Daint. The performance profiling and scalability analysis conducted on the three parent codes allowed to expose their performance issues, such as load imbalance, both in MPI and OpenMP. Two-level load balancing has been successfully applied to SPHYNX to overcome its load imbalance. The performance analysis shapes and drives the design of the SPH-EXA mini-app towards the use of efficient parallelization methods, fault-tolerance mechanisms, and load balancing approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1809.0801

SimGrid VM: Virtual Machine Support for a Simulation Framework of Distributed Systems

International audienceAs real systems become larger and more complex, the use of simulator frameworks grows in our research community. By leveraging them, users can focus on the major aspects of their algorithm, run in-siclo experiments (i.e., simulations), and thoroughly analyze results, even for a large-scale environment without facing the complexity of conducting in-vivo studies (i.e., on real testbeds). Since nowadays the virtual machine (VM) technology has become a fundamental building block of distributed computing environments, in particular in cloud infrastructures, our community needs a full-fledged simulation framework that enables us to investigate large-scale virtualized environments through accurate simulations. To be adopted, such a framework should provide easy-to-use APIs as well as accurate simulation results. In this paper, we present a highly-scalable and versatile simulation framework supporting VM environments. By leveraging SimGrid, a widely-used open-source simulation toolkit, our simulation framework allows users to launch hundreds of thousands of VMs on their simulation programs and control VMs in the same manner as in the real world (e.g., suspend/resume and migrate). Users can execute computation and communication tasks on physical machines (PMs) and VMs through the same SimGrid API, which will provide a seamless migration path to IaaS simulations for hundreds of SimGrid users. Moreover, SimGrid VM includes a live migration model implementing the precopy migration algorithm. This model correctly calculates the migration time as well as the migration traffic, taking account of resource contention caused by other computations and data exchanges within the whole system. This allows user to obtain accurate results of dynamic virtualized systems. We confirmed accuracy of both the VM and the live migration models by conducting several micro-benchmarks under various conditions. Finally, we conclude the article by presenting a first use-case of one consolidation algorithm dealing with a significant number of VMs/PMs. In addition to confirming the accuracy and scalability of our framework, this first scenario illustrates the main interest of SimGrid VM: investigating through in-siclo experiments pros/cons of new algorithms in order to limit expensive in-vivo experiments only to the most promising ones