Performance analysis and optimization of in-situ integration of simulation with data analysis: zipping applications up

This paper targets an important class of applications that requires combining HPC simulations with data analysis for online or real-time scientific discovery. We use the state-of-the-art parallel-IO and data-staging libraries to build simulation-time data analysis workflows, and conduct performance analysis with real-world applications of computational fluid dynamics (CFD) simulations and molecular dynamics (MD) simulations. Driven by in-depth performance inefficiency analysis, we design an end-to-end application-level approach to eliminating the interlocks and synchronizations existent in the present methods. Our new approach employs both task parallelism and pipeline parallelism to reduce synchronizations effectively. In addition, we design a fully asynchronous, fine-grain, and pipelining runtime system, which is named Zipper. Zipper is a multi-threaded distributed runtime system and executes in a layer below the simulation and analysis applications. To further reduce the simulation application's stall time and enhance the data transfer performance, we design a concurrent data transfer optimization that uses both HPC network and parallel file system for improved bandwidth. The scalability of the Zipper system has been verified by a performance model and various empirical large scale experiments. The experimental results on an Intel multicore cluster as well as a Knight Landing HPC system demonstrate that the Zipper based approach can outperform the fastest state-of-the-art I/O transport library by up to 220% using 13,056 processor cores

A Flexible Framework for Asynchronous In Situ and In Transit Analytics for Scientific Simulations

International audienceHigh performance computing systems are today composed of tens of thousands of processors and deep memory hierarchies. The next generation of machines will further increase the unbalance between I/O capabilities and processing power. To reduce the pressure on I/Os, the in situ analytics paradigm proposes to process the data as closely as possible to where and when the data are produced. Processing can be embedded in the simulation code, executed asynchronously on helper cores on the same nodes, or performed in transit on staging nodes dedicated to analytics. Today, software environ- nements as well as usage scenarios still need to be investigated before in situ analytics become a standard practice. In this paper we introduce a framework for designing, deploying and executing in situ scenarios. Based on a com- ponent model, the scientist designs analytics workflows by first developing processing components that are next assembled in a dataflow graph through a Python script. At runtime the graph is instantiated according to the execution context, the framework taking care of deploying the application on the target architecture and coordinating the analytics workflows with the simulation execution. Component coordination, zero- copy intra-node communications or inter-nodes data transfers rely on per-node distributed daemons. We evaluate various scenarios performing in situ and in transit analytics on large molecular dynamics systems sim- ulated with Gromacs using up to 1664 cores. We show in particular that analytics processing can be performed on the fraction of resources the simulation does not use well, resulting in a limited impact on the simulation performance (less than 6%). Our more advanced scenario combines in situ and in transit processing to compute a molecular surface based on the Quicksurf algorithm

Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications

Many scientific problems require multiple distinct computational tasks to be executed in order to achieve a desired solution. We introduce the Ensemble Toolkit (EnTK) to address the challenges of scale, diversity and reliability they pose. We describe the design and implementation of EnTK, characterize its performance and integrate it with two distinct exemplar use cases: seismic inversion and adaptive analog ensembles. We perform nine experiments, characterizing EnTK overheads, strong and weak scalability, and the performance of two use case implementations, at scale and on production infrastructures. We show how EnTK meets the following general requirements: (i) implementing dedicated abstractions to support the description and execution of ensemble applications; (ii) support for execution on heterogeneous computing infrastructures; (iii) efficient scalability up to O(10^4) tasks; and (iv) fault tolerance. We discuss novel computational capabilities that EnTK enables and the scientific advantages arising thereof. We propose EnTK as an important addition to the suite of tools in support of production scientific computing

Potential of I/O aware workflows in climate and weather

The efficient, convenient, and robust execution of data-driven workflows and enhanced data management are essential for productivity in scientific computing. In HPC, the concerns of storage and computing are traditionally separated and optimised independently from each other and the needs of the end-to-end user. However, in complex workflows, this is becoming problematic. These problems are particularly acute in climate and weather workflows, which as well as becoming increasingly complex and exploiting deep storage hierarchies, can involve multiple data centres. The key contributions of this paper are: 1) A sketch of a vision for an integrated data-driven approach, with a discussion of the associated challenges and implications, and 2) An architecture and roadmap consistent with this vision that would allow a seamless integration into current climate and weather workflows as it utilises versions of existing tools (ESDM, Cylc, XIOS, and DDN’s IME). The vision proposed here is built on the belief that workflows composed of data, computing, and communication-intensive tasks should drive interfaces and hardware configurations to better support the programming models. When delivered, this work will increase the opportunity for smarter scheduling of computing by considering storage in heterogeneous storage systems. We illustrate the performance-impact on an example workload using a model built on measured performance data using ESDM at DKRZ

Modeling High-throughput Applications for in situ Analytics

International audienceWith the goal of performing exascale computing, the importance of I/Omanagement becomes more and more critical to maintain system performance.While the computing capacities of machines are getting higher, the I/O capa-bilities of systems do not increase as fast. We are able to generate more databut unable to manage them eciently due to variability of I/O performance.Limiting the requests to the Parallel File System (PFS) becomes necessary. Toaddress this issue, new strategies are being developed such as online in situanalysis. The idea is to overcome the limitations of basic post-mortem dataanalysis where the data have to be stored on PFS rst and processed later.There are several software solutions that allow users to specically dedicatenodes for analysis of data and distribute the computation tasks over dier-ent sets of nodes. Thus far, they rely on a manual resource partitioning andallocation by the user of tasks (simulations, analysis).In this work, we propose a memory-constraint modelization for in situ anal-ysis. We use this model to provide dierent scheduling policies to determineboth the number of resources that should be dedicated to analysis functions,and that schedule eciently these functions. We evaluate them and show theimportance of considering memory constraints in the model. Finally, we discussthe dierent challenges that have to be addressed in order to build automatictools for in situ analytics

Asynchronous In Situ Processing with Gromacs: Taking Advantage of GPUs

International audienceNumerical simulations using supercomputers are producing an ever growing amount of data. Efficient production and analysis of these data are the key to future discoveries. The in situ paradigm is emerging as a promising solution to avoid the I/O bottleneck encountered in the file system for both the simulation and the analytics by treating the data as soon as they are produced in memory. Various strategies and implementations have been proposed in the last years to support in situ treatments with a low impact on the simulation performance. Yet, little efforts have been made when it comes to perform in situ analytics with hybrid simulations supporting accelerators like GPUs. In this article, we propose a study of the in situ strategies with Gromacs, a molecular dynamic simulation code supporting multi-GPUs, as our application target. We specifically focus on the computational resources usage of the machine by the simulation and the in situ analytics. We finally extend the usual in situ placement strategies to the case of in situ analytics running on a GPU and study their impact on both Gromacs performance and the resource usage of the machine. We show in particular that running in situ analytics on the GPU can be a more efficient solution than on the CPU especially when the CPU is the bottleneck of the simulation

Middleware for large scale in situ analytics workflows

The trend to exascale is causing researchers to rethink the entire computa- tional science stack, as future generation machines will contain both diverse hardware environments and run times that manage them. Additionally, the science applications themselves are stepping away from the traditional bulk-synchronous model and are moving towards a more dynamic and decoupled environment where analysis routines are run in situ alongside the large scale simulations. This thesis presents CoApps, a middleware that allows in situ science analytics applications to operate in a location-flexible manner. Additionally, CoApps explores methods to extract information from, and issue management operations to, lower level run times that are managing the diverse hardware expected to be found on next generation exascale machines. This work leverages experience with several extremely scalable applications in materials and fusion, and has been evaluated on machines ranging from local Linux clusters to the supercomputer Titan.Ph.D