Efficient Scalable Computing through Flexible Applications and Adaptive Workloads

In this paper we introduce a methodology for dynamic job reconfiguration driven by the programming model runtime in collaboration with the global resource manager. We improve the system throughput by exploiting malleability techniques (in terms of number of MPI ranks) through the reallocation of resources assigned to a job during its execution. In our proposal, the OmpSs runtime reconfigures the number of MPI ranks during the execution of an application in cooperation with the Slurm workload manager. In addition, we take advantage of OmpSs offload semantics to allow application developers deal with data redistribution. By combining these elements a job is able to expand itself in order to exploit idle nodes or be shrunk if other queued jobs could be initiated. This novel approach adapts the system workload in order to increase the throughput as well as make a smarter use of the underlying resources. Our experiments demonstrate that this approach can reduce the total execution time of a practical workload by more than 40% while reducing the amount of resources by 30%.This work is supported by the Project TIN2014-53495-R and TIN2015-65316-P from MINECO and FEDER. Antonio J. Peña is cofinanced by MINECO under Juan de la Cierva fellowship number IJCI-2015-23266. Special thanks to José I. Aliaga for the conjugate gradient code.Peer ReviewedPostprint (author's final draft

Evaluating worksharing tasks on distributed environments

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hybrid programming is a promising approach to exploit clusters of multicore systems. Our focus is on the combination of MPI and tasking. This hybrid approach combines the low-latency and high throughput of MPI with the flexibility of tasking models and their inherent ability to handle load imbalance. However, combining tasking with standard MPI implementations can be a challenge. The Task-Aware MPI library (TAMPI) eases the development of applications combining tasking with MPI. TAMPI enables developers to overlap computation and communication phases by relying on the tasking data-flow execution model. Using this approach, the original computation that was distributed in many different MPI ranks is grouped together in fewer MPI ranks, and split into several tasks per rank. Nevertheless, programmers must be careful with task granularity. Too fine-grained tasks introduce too much overhead, while too coarse-grained tasks lead to lack of parallelism. An adequate granularity may not always exist, especially in distributed environments where the same amount of work is distributed among many more cores. Worksharing tasks are a special kind of tasks, recently proposed, that internally leverage worksharing techniques. By doing so, a single worksharing task may run in several cores concurrently. Nonetheless, the task management costs remain the same than a regular task. In this work, we study the combination of worksharing tasks and TAMPI on distributed environments using two well known mini-apps: HPCCG and LULESH. Our results show significant improvements using worksharing tasks compared to regular tasks, and to other state-of-the-art alternatives such as OpenMP worksharing.This project is supported by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No.s 754304 (DEEP-EST) and 823767 (PRACE), the Ministry of Economy of Spain through the Severo Ochoa Center of Excellence Program (SEV-2015-0493), by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB) and by the Generalitat de Catalunya (2017-SGR1481). The work has been performed under the Project HPCEUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme; in particular, the author gratefully acknowledges the support of Dr Mark Bull (EPCC) and the computer resources and technical support provided by EPCC.Peer ReviewedPostprint (author's final draft

Modeling Energy Consumption of High-Performance Applications on Heterogeneous Computing Platforms

Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit. Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship between workload and power is less understood. Thus, investigation and analysis of power measurements has been a focal point in this work with the aim to improve the general understanding of energy consumption in the context of HPC. This dissertation investigates power and energy consumption of many different parallel applications on several hardware platforms while varying a number of execution characteristics. Multicore and manycore hardware devices are investigated in homogeneous and heterogeneous computing environments. Further, common techniques for reducing power and energy consumption are employed to each of these devices. Well-known power and performance models have been combined to form the Execution-Phase model, which may be used to quantify energy contributions based on execution phase and has been used to predict energy consumption to within 10%. However, due to limitations in the measurement procedure, a less intrusive approach is required. The Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform analysis technique has been applied in innovative ways to model, analyze, and visualize power and energy measurements. EMD is widely used in other research areas, including earthquake, brain-wave, speech recognition, and sea-level rise analysis and this is the first it has been applied to power traces to analyze the complex interactions occurring within HPC systems. Probability distributions may be used to represent power and energy traces, thereby providing an alternative means of predicting energy consumption while retaining the fact that power is not constant over time. Further, these distributions may be used to define the cost of a workload for a given computing platform

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