Specifying and testing GPU workgroup progress models

As GPU availability has increased and programming support has matured, a wider variety of applications are being ported to these platforms. Many parallel applications contain fine-grained synchronization idioms; as such, their correct execution depends on a degree of relative forward progress between threads (or thread groups). Unfortunately, many GPU programming specifications (e.g. Vulkan and Metal) say almost nothing about relative forward progress guarantees between workgroups. Although prior work has proposed a spectrum of plausible progress models for GPUs, cross-vendor specifications have yet to commit to any model. This work is a collection of tools and experimental data to aid specification designers when considering forward progress guarantees in programming frameworks. As a foundation, we formalize a small parallel programming language that captures the essence of fine-grained synchronization. We then provide a means of formally specifying a progress model, and develop a termination oracle that decides whether a given program is guaranteed to eventually terminate with respect to a given progress model. Next, we formalize a set of constraints that describe concurrent programs that require forward progress to terminate. This allows us to synthesize a large set of 483 progress litmus tests. Combined with the termination oracle, we can determine the expected status of each litmus test -- i.e. whether it is guaranteed to eventually terminate -- under various progress models. We present a large experimental campaign running the litmus tests across 8 GPUs from 5 different vendors. Our results highlight that GPUs have significantly different termination behaviors under our test suite. Most notably, we find that Apple and ARM GPUs do not support the linear occupancy-bound model, as was hypothesized by prior work.

Intelligent programming support system: machine learning feat. Fast development of secure programs.

The article is devoted to the field of software development. The considered scientific contradiction lies in the fact that, on the one hand, the use of manual labor of a programmer is necessary in this area, and on the other hand, the presence of a human factor negatively affects the safety of the resulting code. To resolve the contradiction, it is proposed to use machine learning, which is traditionally used to solve the problem of classification, regression, search for anomalies, clustering, generalization and search for associations. It is shown that the majority of publications on this solution are of a private nature and do not cover the entire spectrum of possibilities. Various ways of automating the programming process using solutions for the specified machine learning problems are considered and substantiated. The demand for a system that combines such methods is indicated; Also, for the first time, its author’s definition is introduced: «Intelligent Programming Support System – a computer automated system based on artificial intelligence technologies, the purpose of which is to help developers of program code in the interests of reducing and simplifying manual labor, as well as increasing the safety of the final product». A comparative analysis of automation methods based on machine learning is given according to 8 criteria that this intelligent system must meet. The ways of further continuation of the research are indicated.

Principles of building competitive motion control systems for highly automated vehicles

The relevance of the issue of development of efficient motion control systems for highly automated vehicles capable to successfully compete with foreign systems of similar purpose is defined by importance of the issue of creation of competitive high-tech products under modern market economy conditions. The research objective was to scientifically justify the building principles for motion control systems for highly automated vehicles providing directed search for solution options for the issue of multi-criterion optimization in the software and hardware space. The research involved methods of system analysis and modern control theory. The research result is a set or complex of building principles for motion control systems for highly automated vehicles providing minimization of hardware environment while keeping observability of all vehicle state coordinates significant for safe control and their dynamic boundaries, as well as ensuring controllability via the channels of traction, braking and direction. The conceptual core or base of such integrated intelligent control systems is mathematical and programming support (software) of indirect measurements of the parameters of motion and control of traction, brakes and direction adapting to the vehicle state and environment changes.

Automatically Identifying the Quality of Developer Chats for Post Hoc Use

Software engineers are crowdsourcing answers to their everyday challenges on Q&A forums (e.g., Stack Overflow) and more recently in public chat communities such as Slack, IRC, and Gitter. Many software-related chat conversations contain valuable expert knowledge that is useful for both mining to improve programming support tools and for readers who did not participate in the original chat conversations. However, most chat platforms and communities do not contain built-in quality indicators (e.g., accepted answers, vote counts). Therefore, it is difficult to identify conversations that contain useful information for mining or reading, i.e., conversations of post hoc quality. In this article, we investigate automatically detecting developer conversations of post hoc quality from public chat channels. We first describe an analysis of 400 developer conversations that indicate potential characteristics of post hoc quality, followed by a machine learning-based approach for automatically identifying conversations of post hoc quality. Our evaluation of 2,000 annotated Slack conversations in four programming communities (python, clojure, elm, and racket) indicates that our approach can achieve precision of 0.82, recall of 0.90, F-measure of 0.86, and MCC of 0.57. To our knowledge, this is the first automated technique for detecting developer conversations of post hoc quality.

Early Childhood Education: Academic and Behavioral Benefits of Prekindergarten Educational Programming

State-funded prekindergarten educational programming for all children is a rarity in the United States. Five states offer no financial support to fund prekindergarten educational programming, and the majority offer partial funding. Only three states provide universally funded prekindergarten educational programming. The purpose of this study is to examine some of the benefits of prekindergarten educational programming to identify reasons to expand the programs and subsequently enroll, at the very least, all economically eligible children. The study analyzed the results of attendance, behavior reports, and test scores to evaluate the academic and behavioral success of six cohorts of students in Grades K–5. All students studied qualified for educational programming support at the level of 100% poverty. The two studied groups are those students who attended the prekindergarten educational programming and those students who did not attend prekindergarten educational programming. The study revealed statistically significant results predominately with behavior and more specifically with office referrals. Students who attended prekindergarten educational programming received fewer office referrals at a statistically significant level compared with those students who did not attend the prekindergarten programming. Academically, the overall results of the data showed that those students who completed prekindergarten educational programming outscored their counterparts who did not attend prekindergarten but not at a statistically significant level. Studies showing the benefits of prekindergarten educational programming may motivate local and state officials to support funding to ensure that at a minimum, all students who qualify based on poverty will have the opportunity to attend prekindergarten educational programming.