MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data

In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms

A Study of Client-based Caching for Parallel I/O

The trend in parallel computing toward large-scale cluster computers running thousands of cooperating processes per application has led to an I/O bottleneck that has only gotten more severe as the the number of processing cores per CPU has increased. Current parallel file systems are able to provide high bandwidth file access for large contiguous file region accesses; however, applications repeatedly accessing small file regions on unaligned file region boundaries continue to experience poor I/O throughput due to the high overhead associated with accessing parallel file system data. In this dissertation we demonstrate how client-side file data caching can improve parallel file system throughput for applications performing frequent small and unaligned file I/O. We explore the impacts of cache page size and cache capacity using the popular FLASH I/O benchmark and explore a novel cache sharing approach that leverages the trend toward multi-core processors. We also explore a technique we call progressive page caching that represents cache data using dynamic data structures rather than fixed-size pages of file data. Finally, we explore a cache aggregation scheme that leverages the high-level file I/O interfaces provided by the PVFS file system to provide further performance enhancements. In summary, our results indicate that a correctly configured middleware-based file data cache can dramatically improve the performance of I/O workloads dominated by small unaligned file accesses. Further, we demonstrate that a well designed cache can offer stable performance even when the selected cache page granularity is not well matched to the provided workload. Finally, we have shown that high-level file system interfaces can significantly accelerate application performance, and interfaces beyond those currently envisioned by the MPI-IO standard could provide further performance benefits

Doctor of Philosophy

dissertationThe increase in computational power of supercomputers is enabling complex scientific phenomena to be simulated at ever-increasing resolution and fidelity. With these simulations routinely producing large volumes of data, performing efficient I/O at this scale has become a very difficult task. Large-scale parallel writes are challenging due to the complex interdependencies between I/O middleware and hardware. Analytic-appropriate reads are traditionally hindered by bottlenecks in I/O access. Moreover, the two components of I/O, data generation from simulations (writes) and data exploration for analysis and visualization (reads), have substantially different data access requirements. Parallel writes, performed on supercomputers, often deploy aggregation strategies to permit large-sized contiguous access. Analysis and visualization tasks, usually performed on computationally modest resources, require fast access to localized subsets or multiresolution representations of the data. This dissertation tackles the problem of parallel I/O while bridging the gap between large-scale writes and analytics-appropriate reads. The focus of this work is to develop an end-to-end adaptive-resolution data movement framework that provides efficient I/O, while supporting the full spectrum of modern HPC hardware. This is achieved by developing technology for highly scalable and tunable parallel I/O, applicable to both traditional parallel data formats and multiresolution data formats, which are directly appropriate for analysis and visualization. To demonstrate the efficacy of the approach, a novel library (PIDX) is developed that is highly tunable and capable of adaptive-resolution parallel I/O to a multiresolution data format. Adaptive resolution storage and I/O, which allows subsets of a simulation to be accessed at varying spatial resolutions, can yield significant improvements to both the storage performance and I/O time. The library provides a set of parameters that controls the storage format and the nature of data aggregation across he network; further, a machine learning-based model is constructed that tunes these parameters for the maximum throughput. This work is empirically demonstrated by showing parallel I/O scaling up to 768K cores within a framework flexible enough to handle adaptive resolution I/O

Topology-Aware Data Aggregation for Intensive I/O on Large-Scale Supercomputers

International audienceReading and writing data efficiently from storage systems is critical for high performance data-centric applications. These I/O systems are being increasingly characterized by complex topologies and deeper memory hierarchies. Effective parallel I/O solutions are needed to scale applications on current and future supercomputers. Data aggregation is an efficient approach consisting of electing some processes in charge of aggregating data from a set of neighbors and writing the aggregated data into storage. Thus, the bandwidth use can be optimized while the contention is reduced. In this work, we take into account the network topology for mapping aggregators and we propose an optimized buffering system in order to reduce the aggregation cost. We validate our approach using micro-benchmarks and the I/O kernel of a large-scale cosmology simulation. We show improvements up to 15× faster for I/O operations compared to a standard implementation of MPI I/O

Methodology for malleable applications on distributed memory systems

A la portada logo BSC(English) The dominant programming approach for scientific and industrial computing on clusters is MPI+X. While there are a variety of approaches within the node, denoted by the ``X'', Message Passing interface (MPI) is the standard for programming multiple nodes with distributed memory. This thesis argues that the OmpSs-2 tasking model can be extended beyond the node to naturally support distributed memory, with three benefits: First, at small to medium scale the tasking model is a simpler and more productive alternative to MPI. It eliminates the need to distribute the data explicitly and convert all dependencies into explicit message passing. It also avoids the complexity of hybrid programming using MPI+X. Second, the ability to offload parts of the computation among the nodes enables the runtime to automatically balance the loads in a full-scale MPI+X program. This approach does not require a cost model, and it is able to transparently balance the computational loads across the whole program, on all its nodes. Third, because the runtime handles all low-level aspects of data distribution and communication, it can change the resource allocation dynamically, in a way that is transparent to the application. This thesis describes the design, development and evaluation of OmpSs-2@Cluster, a programming model and runtime system that extends the OmpSs-2 model to allow a virtually unmodified OmpSs-2 program to run across multiple distributed memory nodes. For well-balanced applications it provides similar performance to MPI+OpenMP on up to 16 nodes, and it improves performance by up to 2x for irregular and unbalanced applications like Cholesky factorization. This work also extended OmpSs-2@Cluster for interoperability with MPI and Barcelona Supercomputing Center (BSC)'s state-of-the-art Dynamic Load Balance (DLB) library in order to dynamically balance MPI+OmpSs-2 applications by transparently offloading tasks among nodes. This approach reduces the execution time of a microscale solid mechanics application by 46% on 64 nodes and on a synthetic benchmark, it is within 10% of perfect load balancing on up to 8 nodes. Finally, the runtime was extended to transparently support malleability for pure OmpSs-2@Cluster programs and interoperate with the Resources Management System (RMS). The only change to the application is to explicitly call an API function to control the addition or removal of nodes. In this regard we additionally provide the runtime with the ability to semi-transparently save and recover part of the application status to perform checkpoint and restart. Such a feature hides the complexity of data redistribution and parallel IO from the user while allowing the program to recover and continue previous executions. Our work is a starting point for future research on fault tolerance. In summary, OmpSs-2@Cluster expands the OmpSs-2 programming model to encompass distributed memory clusters. It allows an existing OmpSs-2 program, with few if any changes, to run across multiple nodes. OmpSs-2@Cluster supports transparent multi-node dynamic load balancing for MPI+OmpSs-2 programs, and enables semi-transparent malleability for OmpSs-2@Cluster programs. The runtime system has a high level of stability and performance, and it opens several avenues for future work.(Español) El modelo de programación dominante para clusters tanto en ciencia como industria es actualmente MPI+X. A pesar de que hay alguna variedad de alternativas para programar dentro de un nodo (indicado por la "X"), el estandar para programar múltiples nodos con memoria distribuida sigue siendo Message Passing Interface (MPI). Esta tesis propone la extensión del modelo de programación basado en tareas OmpSs-2 para su funcionamiento en sistemas de memoria distribuida, destacando 3 beneficios principales: En primer lugar; a pequeña y mediana escala, un modelo basado en tareas es más simple y productivo que MPI y elimina la necesidad de distribuir los datos explícitamente y convertir todas las dependencias en mensajes. Además, evita la complejidad de la programacion híbrida MPI+X. En segundo lugar; la capacidad de enviar partes del cálculo entre los nodos permite a la librería balancear la carga de trabajo en programas MPI+X a gran escala. Este enfoque no necesita un modelo de coste y permite equilibrar cargas transversalmente en todo el programa y todos los nodos. En tercer lugar; teniendo en cuenta que es la librería quien maneja todos los aspectos relacionados con distribución y transferencia de datos, es posible la modificación dinámica y transparente de los recursos que utiliza la aplicación. Esta tesis describe el diseño, desarrollo y evaluación de OmpSs-2@Cluster; un modelo de programación y librería que extiende OmpSs-2 permitiendo la ejecución de programas OmpSs-2 existentes en múltiples nodos sin prácticamente necesidad de modificarlos. Para aplicaciones balanceadas, este modelo proporciona un rendimiento similar a MPI+OpenMP hasta 16 nodos y duplica el rendimiento en aplicaciones irregulares o desbalanceadas como la factorización de Cholesky. Este trabajo incluye la extensión de OmpSs-2@Cluster para interactuar con MPI y la librería de balanceo de carga Dynamic Load Balancing (DLB) desarrollada en el Barcelona Supercomputing Center (BSC). De este modo es posible equilibrar aplicaciones MPI+OmpSs-2 mediante la transferencia transparente de tareas entre nodos. Este enfoque reduce el tiempo de ejecución de una aplicación de mecánica de sólidos a micro-escala en un 46% en 64 nodos; en algunos experimentos hasta 8 nodos se pudo equilibrar perfectamente la carga con una diferencia inferior al 10% del equilibrio perfecto. Finalmente, se implementó otra extensión de la librería para realizar operaciones de maleabilidad en programas OmpSs-2@Cluster e interactuar con el Sistema de Manejo de Recursos (RMS). El único cambio requerido en la aplicación es la llamada explicita a una función de la interfaz que controla la adición o eliminación de nodos. Además, se agregó la funcionalidad de guardar y recuperar parte del estado de la aplicación de forma semitransparente con el objetivo de realizar operaciones de salva-reinicio. Dicha funcionalidad oculta al usuario la complejidad de la redistribución de datos y las operaciones de lectura-escritura en paralelo, mientras permite al programa recuperar y continuar ejecuciones previas. Este es un punto de partida para futuras investigaciones en tolerancia a fallos. En resumen, OmpSs-2@Cluster amplía el modelo de programación de OmpSs-2 para abarcar sistemas de memoria distribuida. El modelo permite la ejecución de programas OmpSs-2 en múltiples nodos prácticamente sin necesidad de modificarlos. OmpSs-2@Cluster permite además el balanceo dinámico de carga en aplicaciones híbridas MPI+OmpSs-2 ejecutadas en varios nodos y es capaz de realizar maleabilidad semi-transparente en programas OmpSs-2@Cluster puros. La librería tiene un niveles de rendimiento y estabilidad altos y abre varios caminos para trabajos futuro.Arquitectura de computador

Resilient gossip-inspired all-reduce algorithms for high-performance computing - Potential, limitations, and open questions

We investigate the usefulness of gossip-based reduction algorithms in a high-performance computing (HPC) context. We compare them to state-of-the-art deterministic parallel reduction algorithms in terms of fault tolerance and resilience against silent data corruption (SDC) as well as in terms of performance and scalability. New gossip-based reduction algorithms are proposed, which significantly improve the state-of-the-art in terms of resilience against SDC. Moreover, a new gossip-inspired reduction algorithm is proposed, which promises a much more competitive runtime performance in an HPC context than classical gossip-based algorithms, in particular for low accuracy requirements.This work has been partially funded by the Spanish Ministry of Science and Innovation [contract TIN2015-65316]; by the Government of Catalonia [contracts 2014-SGR-1051, 2014-SGR-1272]; by the RoMoL ERC Advanced Grant [grant number GA 321253] and by the Vienna Science and Technology Fund (WWTF) through project ICT15-113.Peer ReviewedPostprint (author's final draft

Characterization and modeling of PIDX parallel I/O for performance optimization

pre-printParallel I/O library performance can vary greatly in re- sponse to user-tunable parameter values such as aggregator count, file count, and aggregation strategy. Unfortunately, manual selection of these values is time consuming and dependent on characteristics of the target machine, the underlying file system, and the dataset itself. Some characteristics, such as the amount of memory per core, can also impose hard constraints on the range of viable parameter values. In this work we address these problems by using machine learning techniques to model the performance of the PIDX parallel I/O library and select appropriate tunable parameter values. We characterize both the network and I/O phases of PIDX on a Cray XE6 as well as an IBM Blue Gene/P system. We use the results of this study to develop a machine learning model for parameter space exploration and performance prediction

A visual Analytics System for Optimizing Communications in Massively Parallel Applications

Current and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profile data is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for MiniAMR application on 4,096 MPI processes.This research has been sponsored in part by the U.S. National Science Foundation through grant IIS-1320229, and the U.S. Department of Energy through grants DE-SC0012610 and DE-SC0014917. This research has been funded in part and used resources of the Argonne Leadership Computing Facility at Argonne National Lab- oratory, which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC02-06CH11357. This work was supported in part by the DOE Office of Science, ASCR, under award numbers 57L38, 57L32, 57L11, 57K50, and 508050

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization