Swarnendu Biswas

OCTET: capturing and controlling cross-thread dependences efficiently

Parallel programming is essential for reaping the benefits of parallel hardware, but it is notoriously difficult to develop and debug reliable, scalable software systems. One key challenge is that modern languages and systems provide poor support for ensuring concurrency correctness properties - atomicity, sequential consistency, and multithreaded determinism - because all existing approaches are impractical. Dynamic, software-based approaches slow programs by up to an order of magnitude because capturing and controlling cross-thread dependences (i.e., conflicting accesses to shared memory) requires synchronization at virtually every access to potentially shared memory. This paper introduces a new software-based concurrency control mechanism called OCTET that soundly captures cross-thread dependences and can be used to build dynamic analyses for concurrency correctness. OCTET achieves low overheads by tracking the locality state of each potentially shared object. Non-conflicting accesses conform to the locality state and require no synchronization; only conflicting accesses require a state change and heavyweight synchronization. This optimistic tradeoff leads to significant efficiency gains in capturing cross-thread dependences: a prototype implementation of OCTET in a high-performance Java virtual machine slows real-world concurrent programs by only 26% on average. A dependence recorder, suitable for record & replay, built on top of OCTET adds an additional 5% overhead on average. These results suggest that OCTET can provide a foundation for developing low-overhead analyses that check and enforce concurrency correctness.

Valor: efficient, software-only region conflict exceptions

Data races complicate programming language semantics, and a data race is often a bug. Existing techniques detect data races and define their semantics by detecting conflicts between synchronization-free regions (SFRs). However, such techniques either modify hardware or slow programs dramatically, preventing always-on use today. This paper describes Valor, a sound, precise, software-only region conflict detection analysis that achieves high performance by eliminating the costly analysis on each read operation that prior approaches require. Valor instead logs a regions reads and lazily detects conflicts for logged reads when the region ends. As a comparison, we have also developed FastRCD, a conflict detector that leverages the epoch optimization strategy of the FastTrack data race detector. We evaluate Valor, FastRCD, and FastTrack, showing that Valor dramatically outperforms FastRCD and FastTrack. Valor is the first region conflict detector to provide strong semantic guarantees for racy program executions with under 2X slowdown. Overall, Valor advances the state of the art in always-on support for strong behavioral guarantees for data races.

Hybrid Static–Dynamic Analysis for Statically Bounded Region Serializability

Data races are common. They are difficult to detect, avoid, or eliminate, and programmers sometimes introduce them intentionally. However, shared-memory programs with data races have unexpected, erroneous behaviors. Intentional and unintentional data races lead to atomicity and sequential consistency (SC) violations, and they make it more difficult to understand, test, and verify software. Existing approaches for providing stronger guarantees for racy executions add high run-time overhead and/or rely on custom hardware. This paper shows how to provide stronger semantics for racy programs while providing relatively good performance on commodity systems. A novel hybrid static--dynamic analysis called \emph{EnfoRSer} provides end-to-end support for a memory model called \emph{statically bounded region serializability} (SBRS) that is not only stronger than weak memory models but is strictly stronger than SC. EnfoRSer uses static compiler analysis to transform regions, and dynamic analysis to detect and resolve conflicts at run time. By demonstrating commodity support for a reasonably strong memory model with reasonable overheads, we show its potential as an always-on execution model.

DoubleChecker: efficient sound and precise atomicity checking

Atomicity is a key correctness property that allows programmers to reason about code regions in isolation. However, programs often fail to enforce atomicity correctly, leading to atomicity violations that are difficult to detect. Dynamic program analysis can detect atomicity violations based on an atomicity specification, but existing approaches slow programs substantially. This paper presents DoubleChecker, a novel sound and precise atomicity checker whose key insight lies in its use of two new cooperating dynamic analyses. Its imprecise analysis tracks cross-thread dependences soundly but imprecisely with significantly better performance than a fully precise analysis. Its precise analysis is more expensive but only needs to process a subset of the execution identified as potentially involved in atomicity violations by the imprecise analysis. If DoubleChecker operates in single-run mode, the two analyses execute in the same program run, which guarantees soundness and precision but requires logging program accesses to pass from the imprecise to the precise analysis. In multi-run mode, the first program run executes only the imprecise analysis, and a second run executes both analyses. Multi-run mode trades accuracy for performance; each run of multi-run mode outperforms single-run mode, but can potentially miss violations. We have implemented DoubleChecker and an existing state-of-the-art atomicity checker called Velodrome in a high-performance Java virtual machine. DoubleCheckers single-run mode significantly outperforms Velodrome, while still providing full soundness and precision. DoubleCheckers multi-run mode improves performance further, without significantly impacting soundness in practice. These results suggest that DoubleCheckers approach is a promising direction for improving the performance of dynamic atomicity checking over prior work.

A model-based regression test selection approach for embedded applications

Regression test selection techniques for embedded programs have scarcely been reported in the literature. In this paper, we propose a model-based regression test selection technique for embedded programs. Our proposed model, in addition to capturing the data and control dependence aspects, also represents several additional program features that are important for regression test case selection of embedded programs. These features include control flow, exception handling, message paths, task priorities, state information and object relations. We select a regression test suite based on slicing our proposed graph model. We also propose a genetic algorithm-based technique to select an optimal subset of test cases from the set of regression test cases selected after slicing our proposed model.

Lightweight data race detection for production runs

To detect data races that harm production systems, program analysis must target production runs. However, sound and precise data race detection adds too much run-time overhead for use in production systems. Even existing approaches that provide soundness or precision incur significant limitations. This work addresses the need for soundness (no missed races) and precision (no false races) by introducing novel, efficient production-time analyses that address each need separately. (1) Precise data race detection is useful for developers, who want to fix bugs but loathe false positives. We introduce a precise analysis called RaceChaser that provides low, bounded run-time overhead. (2) Sound race detection benefits analyses and tools whose correctness relies on knowledge of all potential data races. We present a sound, efficient approach called Caper that combines static and dynamic analysis to catch all data races in observed runs. RaceChaser and Caper are useful not only on their own; we introduce a framework that combines these analyses, using Caper as a sound filter for precise data race detection by RaceChaser. Our evaluation shows that RaceChaser and Caper are efficient and effective, and compare favorably with existing state-of-the-art approaches. These results suggest that RaceChaser and Caper enable practical data race detection that is precise and sound, respectively, ultimately leading to more reliable software systems.

A regression test selection technique for embedded software

The current approaches for regression test selection of embedded programs are usually based on data- and control-dependency analyses, often augmented with human reasoning. Existing techniques do not take into account additional execution dependencies which may exist among code elements in such programs due to features such as tasks, task deadlines, task precedences, and intertask communications. In this context, we propose a model-based regression test selection technique for such programs. Our technique first constructs a graph model of the program; the proposed graph model has been designed to capture several characteristics of embedded programs, such as task precedence order, priority, intertask communication, timers, exceptions and interrupt handlers, which we consider important for regression-test selection. Our regression test selection technique selects test cases based on an analysis of the constructed graph model. We have implemented our technique to realize a prototype tool. The experimental results obtained using this tool show that, on average, our approach selects about 28.33% more regression test cases than those selected by a traditional approach. We observed that, on average, 36.36% of the fault-revealing test cases were overlooked by the existing regression test selection technique.

Task Dependency Analysis for Regression Test Selection of Embedded Programs

Execution dependencies arise among the tasks of an embedded program due to issues such as task priority, task precedence, and intertask communication. We argue that execution dependencies among tasks need to be suitably considered in various embedded software engineering activities such as debugging, regression testing, and computation of complexity metrics. In this letter, we discuss how task execution dependencies among real-time tasks can be identified from static code analysis. Subsequently, we briefly describe an application of our analysis to regression test selection.

Avoiding consistency exceptions under strong memory models

Shared-memory languages and systems generally provide weak or undefined semantics for executions with data races. Prior work has proposed memory consistency models that ensure well-defined, easy-to-understand semantics based on region serializability (RS), but the resulting system may throw a consistency exception in the presence of a data race. Consistency exceptions can occur unexpectedly even in well-tested programs, hurting availability and thus limiting the practicality of RS-based memory models. To our knowledge, this paper is the first to consider the problem of availability for memory consistency models that throw consistency exceptions. We first extend existing approaches that enforce RSx, a memory model based on serializability of synchronization-free regions (SFRs), to avoid region conflicts and thus consistency exceptions. These new approaches demonstrate both the potential for and limitations of avoiding consistency exceptions under RSx. To improve availability further, we introduce (1) a new memory model called RIx based on isolation of SFRs and (2) a new approach called Avalon that provides RIx. We demonstrate two variants of Avalon that offer different performance-availability tradeoffs for RIx. An evaluation on real Java programs shows that this works novel approaches are able to reduce consistency exceptions, thereby improving the applicability of strong memory consistency models. Furthermore, the approaches provide compelling points in the performance-availability tradeoff space for memory consistency enforcement. RIx and Avalon thus represent a promising direction for tackling the challenge of availability under strong consistency models that throw consistency exceptions.

Relaxed dependence tracking for parallel runtime support

It is notoriously difficult to achieve both correctness and scalability for many shared-memory parallel programs. To improve correctness and scalability, researchers have developed various kinds of parallel runtime support such as multithreaded record & replay and software transactional memory. Existing forms of runtime support slow programs significantly in order to track an executions cross-thread dependences accurately. This paper investigates the potential for runtime support to hide latency introduced by dependence tracking, by tracking dependences in a relaxed way---meaning that not all dependences are tracked accurately. The key challenge in relaxing dependence tracking is preserving both the programs semantics and the runtime supports guarantees. We present an approach called relaxed dependence tracking (RT) and demonstrate its potential by building two types of RT-based runtime support. Our evaluation shows that RT hides much of the latency incurred by dependence tracking, although RT-based runtime support incurs costs and complexity in order to handle relaxed dependence information. By demonstrating how to relax dependence tracking to hide latency while preserving correctness, this work shows the potential for addressing a key cost of dependence tracking, thus advancing knowledge in the design of parallel runtime support.