khmer: Working with Big Data in Bioinformatics

We introduce design and optimization considerations for the 'khmer' package.Comment: Invited chapter for forthcoming book on Performance of Open Source Application

Proactive bottleneck performance analysis in parallel computing using openMP

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.Comment: 8 Pages,6 figur

Coz: Finding Code that Counts with Causal Profiling

Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spent their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover significant optimization opportunities. This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus "virtually" speeding it up. We present Coz, a causal profiler, which we evaluate on a range of highly-tuned applications: Memcached, SQLite, and the PARSEC benchmark suite. Coz identifies previously unknown optimization opportunities that are both significant and targeted. Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code.Comment: Published at SOSP 2015 (Best Paper Award