Development Of New Code Research Articles

RacerD: compositional static race detection

Automatic static detection of data races is one of the most basic problems in reasoning about concurrency. We present RacerD—a static program analysis for detecting data races in Java programs which is fast, can scale to large code, and has proven effective in an industrial software engineering scenario. To our knowledge, RacerD is the first inter-procedural, compositional data race detector which has been shown to have non-trivial precision and impact. Due to its compositionality, it can analyze code changes quickly, and this allows it to perform continuous reasoning about a large, rapidly changing codebase as part of deployment within a continuous integration ecosystem. In contrast to previous static race detectors, its design favors reporting high-confidence bugs over ensuring their absence. RacerD has been in deployment for over a year at Facebook, where it has flagged over 2500 issues that have been fixed by developers before reaching production. It has been important in enabling the development of new code as well as fixing old code: it helped support conversion of part of the main Facebook Android app from a single-threaded to a multi-threaded architecture. In this paper we describe RacerD’s design, implementation, deployment and impact.

A Mass Spectrometry Proteomics Data Management Platform

Mass spectrometry-based proteomics is increasingly being used in biomedical research. These experiments typically generate a large volume of highly complex data, and the volume and complexity are only increasing with time. There exist many software pipelines for analyzing these data (each typically with its own file formats), and as technology improves, these file formats change and new formats are developed. Files produced from these myriad software programs may accumulate on hard disks or tape drives over time, with older files being rendered progressively more obsolete and unusable with each successive technical advancement and data format change. Although initiatives exist to standardize the file formats used in proteomics, they do not address the core failings of a file-based data management system: (1) files are typically poorly annotated experimentally, (2) files are "organically" distributed across laboratory file systems in an ad hoc manner, (3) files formats become obsolete, and (4) searching the data and comparing and contrasting results across separate experiments is very inefficient (if possible at all). Here we present a relational database architecture and accompanying web application dubbed Mass Spectrometry Data Platform that is designed to address the failings of the file-based mass spectrometry data management approach. The database is designed such that the output of disparate software pipelines may be imported into a core set of unified tables, with these core tables being extended to support data generated by specific pipelines. Because the data are unified, they may be queried, viewed, and compared across multiple experiments using a common web interface. Mass Spectrometry Data Platform is open source and freely available at http://code.google.com/p/msdapl/.

Development of a High-Performance Eigensolver on a Peta-Scale Next-Generation Supercomputer System

For current supercomputer systems, multicore and multisocket processors are required in order to build a system, and choice of interconnection is essential. In addition, for effective development of new code, high-performance, scalable, and reliable numerical software is key. ScaLAPACK and PETSc are software developed for distributed memory parallel computer systems. Real computation requires software that is highly tuned for implementation on new architectures, such as many-core processors. In the present study, we introduce a high-performance, highly scalable eigenvalue solver with the goal of realizing the K-computer system, which is a next-generation supercomputer system. We have developed two versions of this eigenvalue solver, namely, the standard version (eigen_s) and an enhanced-performance version (eigen_sx), both of which were developed on the T2K cluster system housed at the University of Tokyo. Eigen_s uses conventional algorithms, such as Householder tridiagonalization, the divide and conquer (DC) algorithm, and the Householder backtransformation. These algorithms are carefully implemented using a blocking technique and flexible two-dimensional data-distribution in order to reduce the overhead of memory traffic and data transfer, respectively. Eigen_s performs excellently on the T2K system with 4,096 cores (theoretical peak: 37.6 TFLOPS) and exhibits fine performance (3.0 TFLOPS) with a 200,000-dimensional matrix. The enhanced version, eigen_sx, uses more advanced algorithms, such as the narrow-band reduction algorithm, DC for band matrices, and the block Householder back-transformation with WY- representation. Even though this version is still in the test stage, eigen_sxhas realized 4.7 TFLOPS with a 200,000-dimensional matrix.

A Performance Measure for Software Reuse Projects

The efficient development of software has become a critical factor to the success of many organizations. Significant competitive advantage can be realized by reducing the time to complete software development, while maintaining or improving quality levels. Project management activities, including the measurement of performance at the level of the project, can help the organization to both control projects and learn from experience. Traditionally, the productivity of a software development project has been measured as the complexity of code produced per unit of labor involved. An increasing number of organizations, however, are adopting the practice of software reuse, and under such conditions, such a traditional measure of productivity does not fully capture all the relevant elements of the reuse‐driven development environment. This research develops a new performance measure that considers four major components of performance: productivity associated with development of new code, productivity associated with the reuse of previously written code, quality of the reuse decision, and relative value of reuse to the company. To illustrate the feasibility of the measure, a real‐world productivity assessment is presented for an example company, and related implementation issues are discussed.