Sonali Saha

Bio: Sonali Saha is an academic researcher from Indian Institute of Technology Madras. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

On the fly MHP analysis

Abstract: May-Happen-in-Parallel (MHP) analysis forms the basis for many problems of program analysis and program understanding. MHP analysis can also be used by IDEs (integrated-development-environments) to help programmers to refactor parallel-programs, identify racy programs, understand which parts of the program run in parallel, and so on. Since the code keeps changing in the IDE, re-computing the MHP information after every change can be an expensive affair. In this manuscript, we propose a novel scheme to perform incremental MHP analysis (on the fly) of programs written in task parallel languages like X10 to keep the MHP information up to date, in an IDE environment. The key insight of our proposed approach to maintain the MHP information up to date is that we need not rebuild (from scratch) every data structure related to MHP information, after each modification (addition or deletion of statements) in the source code. The idea is to reuse the old MHP information as much as possible and incrementally recompute the MHP information (of a small set of statements) which depends on the statement added/removed. We introduce two new algorithms that deal with addition and removal of parallel constructs like finish, async, atomic, and sequential constructs like loop, if, if-else and other sequential statements, on the fly. Our evaluation shows that our algorithms run much faster than the repeated invocations of the fastest known MHP analysis for X10 programs [Sankar et al. 2016].

IntelliJ IDEA——开发人员利器

Abstract: 在目前的Java IDE市场已经形成三足鼎力：Eclipse、IntelliJ IDEA和NetBeans，对于国内很多开发人员来说，Eclipse众所周知，NetBeans次之，IntelliJ IDEA确很少被人了解。IDEA不被人了解的主要原因是其开发公司不大，而且中文文档比较少，宣传也很少。

OpenMP aware MHP Analysis for Improved Static Data-Race Detection.

Abstract: Data races, a major source of bugs in concurrent programs, can result in loss of manpower and time as well as data loss due to system failures. OpenMP, the de facto shared memory parallelism framework used in the HPC community, also suffers from data races. To detect race conditions in OpenMP programs and improve turnaround time and/or developer productivity, we present a data flow analysis based, fast, static data race checker in the LLVM compiler framework. Our tool can detect races in the presence or absence of explicit barriers, with implicit or explicit synchronization. In addition, our tool effectively works for the OpenMP target offloading constructs and also supports the frequently used OpenMP constructs. We formalize and provide a data flow analysis framework to perform Phase Interval Analysis (PIA) of OpenMP programs. Phase intervals are then used to compute the MHP (and its complement NHP) sets for the programs, which, in turn, are used to detect data races statically. We evaluate our work using multiple OpenMP race detection benchmarks and real world applications. Our experiments show that the checker is comparable to the state-of-the-art in various performance metrics with around 90% accuracy, almost perfect recall, and significantly lower runtime and memory footprint.