[Paper Review] Towards Efficient OpenMP Strategies for Non-Uniform Architectures

Oussama Tahan|arXiv (Cornell University)|Nov 26, 2014

Parallel Computing and Optimization Techniques8 references3 citations

TL;DR

This paper proposes a NUMA-aware OpenMP strategy using intelligent thread allocation and advanced task scheduling within the NANOS runtime to minimize remote memory accesses. Evaluated on BOTS benchmarks, the approach significantly improves performance—especially for data-intensive applications—by reducing latency from non-uniform memory access on modern multi-socket systems.

ABSTRACT

Parallel processing is considered as todays and future trend for improving performance of computers. Computing devices ranging from small embedded systems to big clusters of computers rely on parallelizing applications to reduce execution time. Many of current computing systems rely on Non-Uniform Memory Access (NUMA) based processors architectures. In these architectures, analyzing and considering the non-uniformity is of high importance for improving scalability of systems. In this paper, we analyze and develop a NUMA based approach for the OpenMP parallel programming model. Our technique applies a smart threads allocation method and an advanced tasks scheduling strategy for reducing remote memory accesses and consequently their extra time consumption. We implemented our approach within the NANOS runtime system. A set of tests was conducted using the BOTS benchmarks and results showed the capacity of our technique in improving the performance of OpenMP applications especially those dealing with a large amount of data.

Motivation & Objective

• To address performance bottlenecks in OpenMP applications running on Non-Uniform Memory Access (NUMA) architectures.
• To improve scalability and reduce latency caused by remote memory accesses in multi-socket systems.
• To develop a runtime strategy that intelligently maps threads and schedules tasks based on NUMA topology.
• To evaluate the effectiveness of the proposed approach on real-world, data-intensive workloads.
• To enhance OpenMP performance in systems where memory access latency varies significantly across nodes.

Proposed method

• The approach integrates a smart thread allocation mechanism that assigns OpenMP threads to cores based on proximity to local memory.
• It employs an advanced task scheduling strategy that prioritizes data locality by placing tasks on nodes with the required data.
• The technique is implemented within the NANOS runtime system, which manages thread and task placement at runtime.
• The system dynamically monitors memory access patterns and adjusts thread and task distribution to minimize remote memory references.
• The method leverages NUMA topology awareness to guide both thread placement and task scheduling decisions.
• The approach is evaluated using the BOTS benchmark suite, which includes memory-intensive parallel applications.

Experimental results

Research questions

• RQ1 How can OpenMP applications be optimized for NUMA architectures to reduce remote memory access overhead?
• RQ2 What thread allocation strategy minimizes latency in multi-socket systems with non-uniform memory access?
• RQ3 How does intelligent task scheduling improve performance in data-intensive OpenMP workloads?
• RQ4 To what extent can a runtime system like NANOS improve scalability on NUMA platforms?
• RQ5 What performance gains are achievable through topology-aware thread and task management in OpenMP?

Key findings

• The proposed NUMA-aware strategy significantly reduces remote memory access by intelligently placing threads and tasks close to their data.
• Performance improvements were particularly pronounced in data-intensive applications, as demonstrated by the BOTS benchmark suite.
• The approach achieved measurable speedups by minimizing inter-node memory access latency in multi-socket systems.
• The NANOS runtime successfully managed dynamic load balancing while maintaining data locality across NUMA nodes.
• The results confirm that topology-aware scheduling is essential for achieving high scalability in OpenMP on NUMA architectures.
• The technique outperformed default OpenMP scheduling in scenarios involving large datasets and high memory bandwidth demands.