An Efficient OpenMP Loop Scheduler for Irregular Applications on Large-Scale NUMA Machines

International audienceNowadays shared memory HPC platforms expose a large number of cores organized in a hierarchical way. Parallel application programmers strug- gle to express more and more fine-grain parallelism and to ensure locality on such NUMA platforms. Independent loops stand as a natural source of paral- lelism. Parallel environments like OpenMP provide ways of parallelizing them efficiently, but the achieved performance is closely related to the choice of pa- rameters like the granularity of work or the loop scheduler. Considering that both can depend on the target computer, the input data and the loop workload, the application programmer most of the time fails at designing both portable and ef- ficient implementations. We propose in this paper a new OpenMP loop scheduler, called adaptive, that dynamically adapts the granularity of work considering the underlying system state. Our scheduler is able to perform dynamic load balancing while taking memory affinity into account on NUMA architectures. Results show that adaptive outperforms state-of-the-art OpenMP loop schedulers on memory- bound irregular applications, while obtaining performance comparable to static on parallel loops with a regular workload

Locality-Aware Dynamic Task Graph Scheduling

Dynamic task graph schedulers automatically balance work across processor cores by scheduling tasks among available threads while preserving dependences. In this paper, we design NabbitC, a provably efficient dynamic task graph scheduler that accounts for data locality on NUMA systems. NabbitC allows users to assign a color to each task representing the location (e.g., a processor core) that has the most efficient access to data needed during that node’s execution. NabbitC then automatically adjusts the scheduling so as to preferentially execute each node at the location that matches its color—leading to better locality because the node is likely to make local rather than remote accesses. At the same time, NabbitC tries to optimize load balance and not add too much overhead compared to the vanilla Nabbit scheduler that does not consider locality. We provide a theoretical analysis that shows that NabbitC does not asymptotically impact the scalability of Nabbit . We evaluated the performance of NabbitC on a suite of memory intensive benchmarks. Our experiments indicates that adding locality awareness has a considerable performance advantage compared to the vanilla Nabbit scheduler. In addition, we also compared NabbitC to OpenMP programs for both regular and irregular applications. For regular applications, OpenMP achieves perfect locality and perfect load balance statically. For these benchmarks, NabbitC has a small performance penalty compared to OpenMP due to its dynamic scheduling strategy. For irregular applications, where OpenMP can not achieve locality and load balance simultaneously, we find that NabbitC performs better. Therefore, NabbitC combines the benefits of locality- aware scheduling for regular applications (the forte of static schedulers such as those in OpenMP) and dynamically adapting to load imbalance (the forte of dynamic schedulers such as Cilk Plus, TBB, and Nabbit)

Locality-Aware Concurrency Platforms

Modern computing systems from all domains are becoming increasingly more parallel. Manufacturers are taking advantage of the increasing number of available transistors by packaging more and more computing resources together on a single chip or within a single system. These platforms generally contain many levels of private and shared caches in addition to physically distributed main memory. Therefore, some memory is more expensive to access than other and high-performance software must consider memory locality as one of the first level considerations. Memory locality is often difficult for application developers to consider directly, however, since many of these NUMA affects are invisible to the application programmer and only show up in low performance. Moreover, on parallel platforms, the performance depends on both locality and load balance and these two metrics are often at odds with each other. Therefore, directly considering locality and load balance at the application level may make the application much more complex to program. In this work, we develop locality-conscious concurrency platforms for multiple different structured parallel programming models, including streaming applications, task-graphs and parallel for loops. In all of this work, the idea is to minimally disrupt the application programming model so that the application developer is either unimpacted or must only provide high-level hints to the runtime system. The runtime system then schedules the application to provide good locality of access while, at the same time also providing good load balance. In particular, we address cache locality for streaming applications through static partitioning and developed an extensible platform to execute partitioned streaming applications. For task-graphs, we extend a task-graph scheduling library to guide scheduling decisions towards better NUMA locality with the help of user-provided locality hints. CilkPlus parallel for loops utilize a randomized dynamic scheduler to distribute work which, in many loop based applications, results in poor locality at all levels of the memory hierarchy. We address this issue with a novel parallel for loop implementation that can get good cache and NUMA locality while providing support to maintain good load balance dynamically

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

Structuring the execution of OpenMP applications for multicore architectures

International audienceThe now commonplace multi-core chips have introduced, by design, a deep hierarchy of memory and cache banks within parallel computers as a tradeoff between the user friendliness of shared memory on the one side, and memory access scalability and efficiency on the other side. However, to get high performance out of such machines requires a dynamic mapping of application tasks and data onto the underlying architecture. Moreover, depending on the application behavior, this mapping should favor cache affinity, memory bandwidth, computation synchrony, or a combination of these. The great challenge is then to perform this hardware-dependent mapping in a portable, abstract way. To meet this need, we propose a new, hierarchical approach to the execution of OpenMP threads onto multicore machines. Our ForestGOMP runtime system dynamically generates structured trees out of OpenMP programs. It collects relationship information about threads and data as well. This information is used together with scheduling hints and hardware counter feedback by the scheduler to select the most appropriate threads and data distribution. ForestGOMP features a high-level platform for developing and tuning portable threads schedulers. We present several applications for which we developed specific scheduling policies that achieve excellent speedups on 16-core machines

Reducing the burden of parallel loop schedulers for many-core processors

Funder: FP7 People: Marie‐Curie Actions; Id: http://dx.doi.org/10.13039/100011264; Grant(s): 327744Summary: As core counts in processors increases, it becomes harder to schedule and distribute work in a timely and scalable manner. This article enhances the scalability of parallel loop schedulers by specializing schedulers for fine‐grain loops. We propose a low‐overhead work distribution mechanism for a static scheduler that uses no atomic operations. We integrate our static scheduler with the Intel OpenMP and Cilkplus parallel task schedulers to build hybrid schedulers. Compiler support enables efficient reductions for Cilk, without changing the programming interface of Cilk reducers. Detailed, quantitative measurements demonstrate that our techniques achieve scalable performance on a 48‐core machine and the scheduling overhead is 43% lower than Intel OpenMP and 12.1× lower than Cilk. We demonstrate consistent performance improvements on a range of HPC and data analytics codes. Performance gains are more important as loops become finer‐grain and thread counts increase. We observe consistently 16%–30% speedup on 48 threads, with a peak of 2.8× speedup

Preliminary Experiments with XKaapi on Intel Xeon Phi Coprocessor

International audienceThis paper presents preliminary performance comparisons of parallel applications developed natively for the Intel Xeon Phi accelerator using three different parallel programming environments and their associated runtime systems. We compare Intel OpenMP, Intel CilkPlus and XKaapi together on the same benchmark suite and we provide comparisons between an Intel Xeon Phi coprocessor and a Sandy Bridge Xeon-based machine. Our benchmark suite is composed of three computing kernels: a Fibonacci computation that allows to study the overhead and the scalability of the runtime system, a NQueens application generating irregular and dynamic tasks and a Cholesky factorization algorithm. We also compare the Cholesky factorization with the parallel algorithm provided by the Intel MKL library for Intel Xeon Phi. Performance evaluation shows our XKaapi data-flow parallel programming environment exposes the lowest overhead of all and is highly competitive with native OpenMP and CilkPlus environments on Xeon Phi. Moreover, the efficient handling of data-flow dependencies between tasks makes our XKaapi environment exhibit more parallelism for some applications such as the Cholesky factorization. In that case, we observe substantial gains with up to 180 hardware threads over the state of the art MKL, with a 47% performance increase for 60 hardware threads

ForestGOMP: an efficient OpenMP environment for NUMA architectures

International audienceExploiting the full computational power of current hierarchical multiprocessor machines requires a very careful distribution of threads and data among the underlying non-uniform architecture so as to avoid remote memory access penalties. Directive-based programming languages such as OpenMP, can greatly help to perform such a distribution by providing programmers with an easy way to structure the parallelism of their application and to transmit this information to the runtime system. Our runtime, which is based on a multi-level thread scheduler combined with a NUMA-aware memory manager, converts this information into Scheduling Hints related to thread-memory affinity issues. These hints enable dynamic load distribution guided by application structure and hardware topology, thus helping to achieve performance portability. Several experiments show that mixed solutions (migrating both threads and data) outperform work-stealing based balancing strategies and Next-Touch-based data distribution policies. These techniques provide insights about additional optimizations

Advanced synchronization techniques for task-based runtime systems

Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a challenge, and bottlenecks can manifest in several runtime components. In this paper, we analyze the limiting factors in the scalability of a task-based runtime system and propose individual solutions for each of the challenges, including a wait-free dependency system and a novel scalable scheduler design based on delegation. We evaluate how the optimizations impact the overall performance of the runtime, both individually and in combination. We also compare the resulting runtime against state of the art OpenMP implementations, showing equivalent or better performance, especially for fine-grained tasks.This project is supported by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No.s 754304 (DEEP-EST), by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB and TIN2015-65316P) and by the Generalitat de Catalunya (2017-SGR-1414).Peer ReviewedPostprint (author's final draft