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Much of the past work on dynamic data-race and determinacy-race detection algorithms for task parallelism has focused on structured parallelism with fork-join constructs and, more recently, with future constructs. This paper addresses the problem of dynamic detection of data-races and determinacy-races in task-parallel programs with promises, which are more general than fork-join constructs and futures. The motivation for our work is twofold. First, promises have now become a mainstream synchronization construct, with their inclusion in multiple languages, including C++, JavaScript, and Java. Second, past work on dynamic data-race and determinacy-race detection for task-parallel programs does not apply to programs with promises, thereby identifying a vital need for this work. This paper makes multiple contributions. First, we introduce a featherweight programming language that captures the semantics of task-parallel programs with promises and provides a basis for formally defining determinacy using our semantics. This definition subsumes functional determinacy (same output for same input) and structural determinacy (same computation graph for same input). The main theoretical result shows that the absence of data races is sufficient to guarantee determinacy with both properties. We are unaware of any prior work that established this result for task-parallel programs with promises. Next, we introduce a new Dynamic Race Detector for Promises that we call DRDP. DRDP is the first known race detection algorithm that executes a task-parallel program sequentially without requiring the serial-projection property; this is a critical requirement since programs with promises do not satisfy the serial-projection property in general. Finally, the paper includes experimental results obtained from an implementation of DRDP. The results show that, with some important optimizations introduced in our work, the space and time overheads of DRDP are comparable to those of more restrictive race detection algorithms from past work. To the best of our knowledge, DRDP is the first determinacy race detector for task-parallel programs with promises. 

Browsers are the main way in which most users experience the internet, which makes them a prime target for malicious entities. The best defense for the common user is to keep their browser always up-to-date, installing updates as soon as they are available. Unfortunately, updating a browser is disruptive as it results in loss of user state. Even though modern browsers reopen all pages (tabs) after an update to minimize inconvenience, this approach still loses all local user state in each page (e.g., contents of unsubmitted forms, including associated JavaScript validation state) and assumes that pages can be refreshed and result in the same contents. We believe this is an important barrier that keeps users from updating their browsers as frequently as possible. In this paper, we present the design, implementation, and evaluation of Sinatra, which supports instantaneous browser updates that do not result in any data loss through a novel Multi-Version eXecution (MVX) approach for JavaScript programs, combined with a sophisticated proxy. Sinatra works in pure JavaScript, does not require any browser support, thus works on closed-source browsers, and requires trivial changes to each target page, that can be automated. First, Sinatra captures all the non-determinism available to a JavaScript program (e.g., event handlers executed, expired timers, invocations of Math.random). Our evaluation shows that Sinatra requires 6MB to store such events, and the memory grows at a modest rate of 253KB/s as the user keeps interacting with each page. When an update becomes available, Sinatra transfer the state by re-executing the same set of non-deterministic events on the new browser. During this time, which can be as long as 1.5 seconds, Sinatra uses MVX to allow the user to keep interacting with the old browser. Finally, Sinatra changes the roles in less than 10ms, and the user starts interacting with the new browser, effectively performing a browser update with zero downtime and no loss of state. 

The C++ Standard Library is a valuable collection of generic algorithms and data structures that improves the usability and reliability of C++ software. Graph algorithms and data structures are notably absent from the standard library, and previous attempts to fill this gap have not gained widespread adoption. In this paper we show that the richness of graph algorithms and data structures can in fact be captured by straightforward composition of existing C++ mechanisms. Generic programming is algorithm-oriented. Accordingly, we apply a systematic approach to analyzing a broad set of graph algorithms, "lift" unnecessary constraints from them, and organize the resulting set of minimal common type requirements, i.e., concepts, for defining their interfaces. By using the newly available ranges and concepts in C++20, the type requirements for generic graph algorithms can be succinctly expressed. The generic algorithms and data structures resulting from our analysis are realized in NWGraph, a modern, composable, and extensible C++ library.