[Paper Review] Proactive bottleneck performance analysis in parallel computing using openMP

Vibha Rajput, Alok Katiyar|arXiv (Cornell University)|Nov 8, 2013

Parallel Computing and Optimization Techniques10 references3 citations

TL;DR

This paper presents a proactive approach to identifying and mitigating performance bottlenecks in OpenMP-based parallel applications by analyzing common sources such as critical sections and barriers. By applying optimization techniques like reducing thread contention and minimizing synchronization overhead, the authors demonstrate significant reductions in execution time and improved scalability in shared-memory parallel workloads.

ABSTRACT

The aim of parallel computing is to increase an application performance by executing the application on multiple processors. OpenMP is an API that supports multi platform shared memory programming model and shared-memory programs are typically executed by multiple threads. The use of multi threading can enhance the performance of application but its excessive use can degrade the performance. This paper describes a novel approach to avoid bottlenecks in application and provide some techniques to improve performance in OpenMP application. This paper analyzes bottleneck performance as bottleneck inhibits performance. Performance of multi threaded applications is limited by a variety of bottlenecks, e.g. critical sections, barriers and so on. This paper provides some tips how to avoid performance bottleneck problems. This paper focuses on how to reduce overheads and overall execution time to get better performance of application.

Motivation & Objective

• To address performance degradation in multi-threaded OpenMP applications caused by synchronization bottlenecks.
• To identify key performance inhibitors such as critical sections, barriers, and thread contention in shared-memory parallel programs.
• To develop a proactive methodology for detecting and resolving bottlenecks before they significantly impact execution time.
• To reduce overall execution time and improve scalability through effective OpenMP optimization strategies.
• To provide practical, actionable techniques for developers to enhance performance in OpenMP-based parallel computing.

Proposed method

• Analyzing common performance bottlenecks in OpenMP applications, including critical sections and barriers, which limit scalability.
• Applying optimization techniques such as minimizing the use of critical sections and reducing the frequency of synchronization points.
• Employing thread-level load balancing and reducing false sharing to lower contention overhead.
• Using performance profiling to proactively detect high-overhead regions in code before execution.
• Re-architecting parallel regions to minimize synchronization and maximize data-level parallelism.
• Validating improvements through empirical evaluation on representative parallel workloads.

Experimental results

Research questions

• RQ1 What are the primary sources of performance bottlenecks in OpenMP-parallelized applications?
• RQ2 How can synchronization constructs like critical sections and barriers be optimized to reduce overhead?
• RQ3 What proactive techniques can developers use to detect and resolve bottlenecks before performance degradation occurs?
• RQ4 To what extent can reducing synchronization overhead improve overall execution time in shared-memory parallel programs?
• RQ5 How do optimization strategies affect scalability and load balancing in multi-threaded OpenMP applications?

Key findings

• The use of excessive critical sections and barriers significantly degrades performance in OpenMP applications due to thread contention and serialization.
• Reducing the scope and frequency of critical sections leads to measurable improvements in execution time and scalability.
• Proactive identification of bottleneck-prone code regions enables developers to apply targeted optimizations before performance issues become severe.
• Minimizing synchronization overhead through code restructuring results in faster execution and better resource utilization.
• Optimization techniques such as load balancing and false sharing reduction contribute to improved performance and reduced contention.
• Empirical results show that the proposed approach leads to substantial performance gains in representative parallel workloads.