A design method for supporting the development and integration of ARTful global schedulers into multiple programming models

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2019.Plataformas de execução em ambientes de alto desempenho estão tornando-se cada vez mais diversas com o desenvolvimento de novas arquiteturas e ferramentas para se beneficiar do paralelismo inerente das aplicações. Estas novas opções de ferramentas oferecem possibilidades de melhoria no desempenho de aplicações científicas e de engenharia. A crescente gama de plataformas torna mais complexa a distribuição das tarefas da aplicação no ambiente, passo que deve ser gerido pelos sistemas de execução e seus balanceadores de carga, de modo a não prejudicar a portabilidade da aplicação. No entanto, o desenvolvimento e implantação de novos escalonadores de tarefas em sistemas amplamente utilizados na indústria como OpenMP e MPI sofrem com a falta de suporte de frameworks. Este trabalho propõe uma biblioteca, MOGSLib, para auxiliar o desenvolvimento e implantação de escalonadores globais em diferentes sistemas de execução de alto desempenho. MOGSLib aplica abstrações reutilizáveis, independentes e testáveis na forma de classes template em C++ para representar as políticas de escalonamento, sua relação com o sistema de execução e a taxonomia da solução de escalonamento. Nós avaliamos o sobrecusto de nossas estratégias portáveis em comparação com suas versões nativas nos sistemas de execução em dois ambientes distintos, os sistemas Charm++ e OpenMP. Em nosso experimentos, conseguimos dar suporte à definição de estratégias de escalonamento do usuário e sua implantação como escalonadores de laço na LibGOMP e balanceadores de carga no Charm++ através da seleção de implementação das abstrações que os compõem. Nós verificamos que nossas versões dos escalonadores oferecem tempos de execução de aplicação equivalentes aos balanceadores nativos para a classe de escalonadores centralizados e cientes de carga aplicados em kernels de dinâmica molecular. Por fim, a flexibilidade de incorporar funcionalidades e políticas de escalonamento do usuário em sistemas de execução com modificações limitadas nos códigos de sistemas de execução mostra que é possível construir balanceadores de carga flexíveis com pouco sobrecusto até para ambientes de alto desempenho.Abstract: Execution platforms for high performance computing are becoming diverse as a result of new architectures and tools to benefit from the parallel behavior of applications. These new options showcase performance enhancing opportunities for scientific and engineering applications. The execution platform diversity and the mapping of an application's tasks must be implicitly handled by runtime systems and their global schedulers as to enable the application performance and implementation portability. However, the development and integration of novel global schedulers into industry standards systems like OpenMP and MPI lack framework support. This work proposes a library, MOGSLib, to support the development and integration of global schedulers into different high performance runtime systems. MOGSLib employs reusable, independent and testable abstractions represented as C++ template structures to express scheduling policies, their relationship to runtime systems and the scheduling solution taxonomy. This approach allows a bottom-up development process based on the incremental composition of abstractions through template specializations. We evaluate the overhead of employing our portable policies in comparison to their system-native counterparts in two environments, the Charm++ and OpenMP systems. Throughout our experiments we achieved development support for user-defined scheduling policies that can be implanted both as loop schedulers in LibGOMP and load balancers in Charm++ through the selection of abstraction implementations. We leveraged the overhead by employing workload-aware policies on molecular dynamics kernels which resulted in equivalent application makespan for both native and the MOGSLib scheduler versions. Ultimately, the flexibility to incorporate user-defined structures and scheduling policies into runtime systems with limited alterations into runtime system code bases hint that the definition of flexible global schedulers is available with neglible overheads even for high performance environments

A Comprehensive Performance Evaluation of the BinLPT Workload-Aware Loop Scheduler

International audienceWorkload-aware loop schedulers were introduced to deliver better performance than classical loop scheduling strategies. However, they presented limitations such as inexible built-in workload estimators and suboptimal chunk scheduling. Targeting these challenges, we proposed previously a workload-aware scheduling strategy called BinLPT, which relies on three features: (i) user-supplied estimations of the workload of the loop; (ii) a greedy heuristic that adaptively partitions the iteration space in several chunks; and (iii) a scheduling scheme based on the Longest Processing Time (LPT) rule and on-demand technique. In this paper, we present two new contributions to the state-of-the-art. First, we introduce a multiloop support feature to BinLPT, which enables the reuse of estimations across loops. Based on this feature, we integrated BinLPT into a real-world elastodynamics application, and we evaluated it running on a supercomputer. Second, we present an evaluation of BinLPT using simulations as well as synthetic and application kernels. We carried out this analysis on a large-scale NUMA machine under a variety of workloads. Our results revealed that BinLPT is better at balancing the workloads of the loop iterations and this behavior improves as the algorithmic complexity of the loop increases. Overall, BinLPT delivers up to 37.15% and 9.11% better performance than well-known loop scheduling strategies, for the application kernels and the elastodynamics simulation, respectively

Automated Scheduling Algorithm Selection and Chunk Parameter Calculation in OpenMP

Increasing node and cores-per-node counts in supercomputers render scheduling and load balancing critical for exploiting parallelism. OpenMP applications can achieve high performance via careful selection of scheduling kind and chunk parameters on a per-loop, per-application, and per-system basis from a portfolio of advanced scheduling algorithms (Korndörfer et al. , 2022). This selection approach is time-consuming, challenging, and may need to change during execution. We propose Auto4OMP , a novel approach for automated load balancing of OpenMP applications. With Auto4OMP, we introduce three scheduling algorithm selection methods and an expert-defined chunk parameter for OpenMP's schedule clause's kind and chunk , respectively. Auto4OMP extends the OpenMP schedule(auto) and chunk parameter implementation in LLVM's OpenMP runtime library to automatically select a scheduling algorithm and calculate a chunk parameter during execution. Loop characteristics are inferred in Auto4OMP from the loop execution over the application's time-steps. The experiments performed in this work show that Auto4OMP improves applications performance by up to 11 % compared to LLVM's schedule(auto) implementation and outperforms manual selection. Auto4OMP improves MPI+OpenMP applications performance by explicitly minimizing thread- and implicitly reducing process-load imbalance