Execution Of Multithreaded Applications Research Articles

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Exascale computing systems will exhibit high degrees of hierarchical parallelism, with thousands of computing nodes and hundreds of cores per node. Efficiently exploiting hierarchical parallelism is challenging due to load imbalance that arises at multiple levels. OpenMP is the most widely-used standard for expressing and exploiting the ever-increasing node-level parallelism. The scheduling options in OpenMP are insufficient to address the load imbalance that arises during the execution of multithreaded applications. The limited scheduling options in OpenMP hinder research on novel scheduling techniques which require comparison with others from the literature. This work introduces LB4OMP, an open-source dynamic load balancing library that implements successful scheduling algorithms from the literature. LB4OMP is a research infrastructure designed to spur and support present and future scheduling research, for the benefit of multithreaded applications performance. Through an extensive performance analysis campaign, we assess the effectiveness and demystify the performance of all loop scheduling techniques in the library. We show that, for numerous applications-systems pairs, the scheduling techniques in LB4OMP outperform the scheduling options in OpenMP. Node-level load balancing using LB4OMP leads to reduced cross-node load imbalance and to improved MPI+OpenMP applications performance, which is critical for Exascale computing.

ADVANCED SCHEDULER FOR COOPERATIVE EXECUTION OF THREADS ON MULTI-CORE SYSTEM

Three architectures of the cooperative thread scheduler in a multithreaded application that is executed on a multi-core system are considered. Architecture A0 is based on the synchronization and scheduling facilities, which are provided by the operating system. Architecture A1 introduces a new synchronization primitive and a single queue of the blocked threads in the scheduler, which reduces the interaction activity between the threads and operating system, and significantly speed up the processes of blocking and unblocking the threads. Architecture A2 replaces the single queue of blocked threads with dedicated queues, one for each of the synchronizing primitives, extends the number of internal states of the primitive, reduces the inter- dependence of the scheduling threads, and further significantly speeds up the processes of blocking and unblocking the threads. All scheduler architectures are implemented on Windows operating systems and based on the User Mode Scheduling. Important experimental results are obtained for multithreaded applications that implement two blocked parallel algorithms of solving the linear algebraic equation systems by the Gaussian elimination. The algorithms differ in the way of the data distribution among threads and by the thread synchronization models. The number of threads varied from 32 to 7936. Architecture A1 shows the acceleration of up to 8.65% and the architecture A2 shows the acceleration of up to 11.98% compared to A0 architecture for the blocked parallel algorithms computing the triangular form and performing the back substitution. On the back substitution stage of the algorithms, architecture A1 gives the acceleration of up to 125%, and architecture A2 gives the acceleration of up to 413% compared to architecture A0. The experiments clearly show that the proposed architectures, A1 and A2 outperform A0 depending on the number of thread blocking and unblocking operations, which happen during the execution of multi-threaded applications. The conducted computational experiments demonstrate the improvement of parameters of multithreaded applications on a heterogeneous multi-core system due the proposed advanced versions of the thread scheduler.

Intention preservation in deterministic multithreading: a partial solution

Deterministic multithreading is a promising new research area that aims to enable reproducible deterministic execution of multithreaded applications. Some of the proposed approaches can render data races deterministic. In this study, the authors introduce the intention preservation problem in multithreaded applications and point out that existing deterministic multithreading approaches do not always preserve the program intention. While it is desirable to automatically capture the full intention of a program and preserve it in deterministic multithreading, it might be an intractable problem. Instead, they provide a partial solution for two specific cases. In the first case, they aim to preserve the program intention for read and write operations on primitive types by tracking read–write causality. In the second case, they preserve the program intention for concurrent write operations on ordered collection types by applying an operational transformation.