Dynamically detecting and tolerating IF-Condition Data Races

An IF-Condition Invariance Violation (ICIV) occurs when, after a thread has computed the control expression of an IF statement and while it is executing the THEN or ELSE clauses, another thread updates variables in the IF’s control expression. An ICIV can be easily detected, and is likely to be a sign of a concurrency bug in the code. Typically, the ICIV is caused by a data race, which we call IF-Condition Data Race (ICR). In this paper, we analyze the data races reported in the bug databases of popular software systems and show that ICRs occur relatively often. Then, we present two techniques to handle ICRs dynamically. They rely on simple code transformations and, in one case, additional hardware help. One of them (SW-IF) detects the races, while the other (HW-IF) detects and prevents them. We evaluate SW-IF and HW-IF using a variety of applica-tions. We show that these new techniques are effective at finding new data race bugs and run with low overhead. Specifically, HW-IF finds 5 new (unreported) race bugs and SW-IF finds 3 of them. In addition, 8-threaded executions of SPLASH-2 codes show that, on average, SW-IF adds 2 % execution overhead, while HW-IF adds less than 1%. 1

New hardware support transactional memory and parallel debugging in multicore processors

This thesis contributes to the area of hardware support for parallel programming by introducing new hardware elements in multicore processors, with the aim of improving the performance and optimize new tools, abstractions and applications related with parallel programming, such as transactional memory and data race detectors. Specifically, we configure a hardware transactional memory system with signatures as part of the hardware support, and we develop a new hardware filter for reducing the signature size. We also develop the first hardware asymmetric data race detector (which is also able to tolerate them), based also in hardware signatures. Finally, we propose a new module of hardware signatures that solves some of the problems that we found in the previous tools related with the lack of flexibility in hardware signatures

PARSNIP: Performant Architecture for Race Safety with No Impact on Precision

Data race detection is a useful dynamic analysis for multithreaded programs that is a key building block in record-and-replay, enforcing strong consistency models, and detecting concurrency bugs. Existing software race detectors are precise but slow, and hardware support for precise data race detection relies on assumptions like type safety that many programs violate in practice. We propose PARSNIP, a fully precise hardware-supported data race detector. PARSNIP exploits new insights into the redundancy of race detection metadata to reduce storage overheads. PARSNIP also adopts new race detection metadata encodings that accelerate the common case while preserving soundness and completeness. When bounded hardware resources are exhausted, PARSNIP falls back to a software race detector to preserve correctness. PARSNIP does not assume that target programs are type safe, and is thus suitable for race detection on arbitrary code. Our evaluation of PARSNIP on several PARSEC benchmarks shows that performance overheads range from negligible to 2.6x, with an average overhead of just 1.5x. Moreover, Parsnip outperforms the state-of-the-art Radish hardware race detector by 4.6x

Finding and Tolerating Concurrency Bugs.

Shared-memory multi-threaded programming is inherently more difficult than single-threaded programming. The main source of complexity is that, the threads of an application can interleave in so many different ways. To ensure correctness, a programmer has to test all possible thread interleavings, which, however, is impractical. Many rare thread interleavings remain untested in production systems, and they are the major cause for a majority of concurrency bugs. Given that untested interleavings are the major cause of a majority of the concurrency bugs, this dissertation explores two possible ways to tackle concurrency bugs in this dissertation. One is to expose untested interleavings during testing to find concurrency bugs. The other is to avoid untested interleavings during production runs to tolerate concurrency bugs. The key is an efficient and effective way to encode and remember tested interleavings. This dissertation first discusses two hypotheses about concurrency bugs: the small scope hypothesis and the value independent hypothesis. Based on these two hypotheses, this dissertation defines a set of interleaving patterns, called interleaving idioms, which are used to encode tested interleavings. The empirical analysis shows that the idiom based interleaving encoding scheme is able to represent most of the concurrency bugs that are used in the study. Then, this dissertation discusses an open source testing tool called Maple. It memoizes tested interleavings and actively seeks to expose untested interleavings. The results show that Maple is able to expose concurrency bugs and expose interleavings faster than other conventional testing techniques. Finally, this dissertation discusses two parallel runtime system designs which seek to avoid untested interleavings during production runs to tolerate concurrency bugs. Avoiding untested interleavings significantly improve correctness because most of the concurrency bugs are caused by untested interleavings. Also, the performance overhead for disallowing untested interleavings is low as commonly occuring interleavings should have been tested in a well-tested program.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99765/1/jieyu_1.pd

Efficient Precise Dynamic Data Race Detection For Cpu And Gpu

Data races are notorious bugs. They introduce non-determinism in programs behavior, complicate programs semantics, making it challenging to debug parallel programs. To make parallel programming easier, efficient data race detection has been a research topic in the last decades. However, existing data race detectors either sacrifice precision or incur high overhead, limiting their application to real-world applications and scenarios. This dissertation proposes approaches to improve the performance of dynamic data race detection without undermining precision, by identifying and removing metadata redundancy dynamically. This dissertation also explores ways to make it practical to detect data races dynamically for GPU programs, which has a disparate programming and execution model from CPU workloads. Further, this dissertation shows how the structured synchronization model in GPU programs can simplify the algorithm design of data race detection for GPU, and how the unique patterns in GPU workloads enable an efficient implementation of the algorithm, yielding a high-performance dynamic data race detector for GPU programs

Putting culture under the spotlight reveals universal information use for face recognition

Background: Eye movement strategies employed by humans to identify conspecifics are not universal. Westerners predominantly fixate the eyes during face recognition, whereas Easterners more the nose region, yet recognition accuracy is comparable. However, natural fixations do not unequivocally represent information extraction. So the question of whether humans universally use identical facial information to recognize faces remains unresolved. Methodology/Principal Findings: We monitored eye movements during face recognition of Western Caucasian (WC) and East Asian (EA) observers with a novel technique in face recognition that parametrically restricts information outside central vision. We used ‘Spotlights’ with Gaussian apertures of 2°, 5° or 8° dynamically centered on observers’ fixations. Strikingly, in constrained Spotlight conditions (2°, 5°) observers of both cultures actively fixated the same facial information: the eyes and mouth. When information from both eyes and mouth was simultaneously available when fixating the nose (8°), as expected EA observers shifted their fixations towards this region. Conclusions/Significance: Social experience and cultural factors shape the strategies used to extract information from faces, but these results suggest that external forces do not modulate information use. Human beings rely on identical facial information to recognize conspecifics, a universal law that might be dictated by the evolutionary constraints of nature and not nurture

How Double-Fetch Situations turn into Double-Fetch Vulnerabilities: A Study of Double Fetches in the Linux Kernel

We present the first static approach that systematically detects potential double-fetch vulnerabilities in the Linux kernel. Using a pattern-based analysis, we identified 90 double fetches in the Linux kernel. 57 of these occur in drivers, which previous dynamic approaches were unable to detect without access to the corresponding hardware. We manually investigated the 90 occurrences, and inferred three typical scenarios in which double fetches occur. We discuss each of them in detail. We further developed a static analysis, based on the Coccinelle matching engine, that detects double-fetch situations which can cause kernel vulnerabilities. When applied to the Linux, FreeBSD, and Android kernels, our approach found six previously unknown double-fetch bugs, four of them in drivers, three of which are exploitable double-fetch vulnerabilities. All of the identified bugs and vulnerabilities have been confirmed and patched by maintainers. Our approach has been adopted by the Coccinelle team and is currently being integrated into the Linux kernel patch vetting. Based on our study, we also provide practical solutions for anticipating double-fetch bugs and vulnerabilities. We also provide a solution to automatically patch detected double-fetch bugs