The development of cloud computing delivery models inspires the emergence of cloud-
native computing. Cloud-native computing, as the most influential development principle for …

The traditional approach for performance debugging relies upon performance profilers (eg,
gprof, VTune) that provide average function runtime information. These aggregate statistics …

Performance efficiency and scalability are the major design goals for high performance
computing (HPC) applications. However, it is challenging to achieve high efficiency and …

Performance variance is a serious problem for parallel applications, which can cause
performance degradation and make applications' behavior hard to understand. Therefore …

Sampling-based Feedback Directed Optimization (FDO) methods like AutoFDO and BOLT
that employ profiles collected in live production environments, are commonly used in …

We present a source-to-source transformation framework, Gocc, that consumes lock-based
pessimistic concurrency programs in the Go language and transforms them into optimistic …

For parallel applications, performance variance is a critical issue that can degrade
performance and make applications' behavior difficult to explain. Therefore, users and …

The computing landscape is undergoing rapid evolution to meet the demand in
dataintensive applications and grand challenging scientific problems. Figure 1.1 illustrates …

Continuous scaling-up of high-performance computing systems has brought challenges to
the debugging and tuning of large-scale parallel programs. Firstly, to locate bugs in a …

The performance variance detection approach in Chap. 7 relies on nontrivial source code
analysis that is impractical for production-run parallel applications. In this chapter, we further …