SmartTrack: Efficient Predictive Race Detection

Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect more data races in an analyzed execution than FastTrack detects, but at significantly higher performance cost. This paper presents SmartTrack, an algorithm that optimizes predictive race detection analyses, including two analyses from prior work and a new analysis introduced in this paper. SmartTrack's algorithm incorporates two main optimizations: (1) epoch and ownership optimizations from prior work, applied to predictive analysis for the first time; and (2) novel conflicting critical section optimizations introduced by this paper. Our evaluation shows that SmartTrack achieves performance competitive with FastTrack-a qualitative improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228 SmartTrack: Efficient Predictive Race Detectio

Enabling Program Analysis Through Deterministic Replay and Optimistic Hybrid Analysis

As software continues to evolve, software systems increase in complexity. With software systems composed of many distinct but interacting components, today’s system programmers, users, and administrators find themselves requiring automated ways to find, understand, and handle system mis-behavior. Recent information breaches such as the Equifax breach of 2017, and the Heartbleed vulnerability of 2014 show the need to understand and debug prior states of computer systems. In this thesis I focus on enabling practical entire-system retroactive analysis, allowing programmers, users, and system administrators to diagnose and understand the impact of these devastating mishaps. I focus primarly on two techniques. First, I discuss a novel deterministic record and replay system which enables fast, practical recollection of entire systems of computer state. Second, I discuss optimistic hybrid analysis, a novel optimization method capable of dramatically accelerating retroactive program analysis. Record and replay systems greatly aid in solving a variety of problems, such as fault tolerance, forensic analysis, and information providence. These solutions, however, assume ubiquitous recording of any application which may have a problem. Current record and replay systems are forced to trade-off between disk space and replay speed. This trade-off has historically made it impractical to both record and replay large histories of system level computation. I present Arnold, a novel record and replay system which efficiently records years of computation on a commodity hard-drive, and can efficiently replay any recorded information. Arnold combines caching with a unique process-group granularity of recording to produce both small, and quickly recalled recordings. My experiments show that under a desktop workload, Arnold could store 4 years of computation on a commodity 4TB hard drive. Dynamic analysis is used to retroactively identify and address many forms of system mis-behaviors including: programming errors, data-races, private information leakage, and memory errors. Unfortunately, the runtime overhead of dynamic analysis has precluded its adoption in many instances. I present a new dynamic analysis methodology called optimistic hybrid analysis (OHA). OHA uses knowledge of the past to predict program behaviors in the future. These predictions, or likely invariants are speculatively assumed true by a static analysis. This creates a static analysis which can be far more accurate than its traditional counterpart. Once this predicated static analysis is created, it is speculatively used to optimize a final dynamic analysis, creating a far more efficient dynamic analysis than otherwise possible. I demonstrate the effectiveness of OHA by creating an optimistic hybrid backward slicer, OptSlice, and optimistic data-race detector OptFT. OptSlice and OptFT are just as accurate as their traditional hybrid counterparts, but run on average 8.3x and 1.6x faster respectively. In this thesis I demonstrate that Arnold’s ability to record and replay entire computer systems, combined with optimistic hybrid analysis’s ability to quickly analyze prior computation, enable a practical and useful entire system retroactive analysis that has been previously unrealized.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144052/1/ddevec_1.pd

Understanding Persistent-Memory Related Issues in the Linux Kernel

Persistent memory (PM) technologies have inspired a wide range of PM-based system optimizations. However, building correct PM-based systems is difficult due to the unique characteristics of PM hardware. To better understand the challenges as well as the opportunities to address them, this paper presents a comprehensive study of PM-related issues in the Linux kernel. By analyzing 1,553 PM-related kernel patches in-depth and conducting experiments on reproducibility and tool extension, we derive multiple insights in terms of PM patch categories, PM bug patterns, consequences, fix strategies, triggering conditions, and remedy solutions. We hope our results could contribute to the development of robust PM-based storage systemsComment: ACM TRANSACTIONS ON STORAGE(TOS'23

CARISMA: a context-sensitive approach to race-condition sample-instance selection for multithreaded applications

Dynamic race detectors can explore multiple thread schedules of a multithreaded program over the same input to detect data races. Although existing sampling-based precise race detectors reduce overheads effectively so that lightweight precise race detection can be performed in testing or post-deployment environments, they are ineffective in detecting races if the sampling rates are low. This paper presents CARISMA to address this problem. CARISMA exploits the insight that along an execution trace, a program may potentially handle many accesses to the memory locations created at the same site for similar purposes. Iterating over multiple execution trials of the same input, CARISMA estimates and distributes the sampling budgets among such location creation sites, and probabilistically collects a fraction of all accesses to the memory locations associated with such sites for subsequent race detection. Our experiment shows that, compared with PACER on the same platform and at the same sampling rate (such as 1%), CARISMA is significantly more effective. © 2012 ACM.postprin

Provably good race detection that runs in parallel

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 93-98).A multithreaded parallel program that is intended to be deterministic may exhibit nondeterminism clue to bugs called determinacy races. A key capability of race detectors is to determine whether one thread executes logically in parallel with another thread or whether the threads must operate in series. This thesis presents two algorithms, one serial and one parallel, to maintain the series-parallel (SP) relationships "on the fly" for fork-join multithreaded programs. For a fork-join program with T1 work and a critical-path length of T[infinity], the serial SP-Maintenance algorithm runs in O(T1) time. The parallel algorithm executes in the nearly optimal O(T1/P + PT[infinity]) time, when run on P processors and using an efficient scheduler. These SP-maintenance algorithms can be incorporated into race detectors to get a provably good race detector that runs in parallel. This thesis describes an efficient parallel race detector I call Nondeterminator-3. For a fork-join program T1 work, critical-path length T[infinity], and v shared memory locations, the Nondeterminator-3 runs in O(T1/P + PT[infinity] lg P + min [(T1 lg P)/P, vT[infinity] Ig P]) expected time, when run on P processors and using an efficient scheduler.by Jeremy T. Fineman.S.M

Doctor of Philosophy

dissertationHigh Performance Computing (HPC) on-node parallelism is of extreme importance to guarantee and maintain scalability across large clusters of hundreds of thousands of multicore nodes. HPC programming is dominated by the hybrid model "MPI + X", with MPI to exploit the parallelism across the nodes, and "X" as some shared memory parallel programming model to accomplish multicore parallelism across CPUs or GPUs. OpenMP has become the "X" standard de-facto in HPC to exploit the multicore architectures of modern CPUs. Data races are one of the most common and insidious of concurrent errors in shared memory programming models and OpenMP programs are not immune to them. The OpenMP-provided ease of use to parallelizing programs can often make it error-prone to data races which become hard to find in large applications with thousands lines of code. Unfortunately, prior tools are unable to impact practice owing to their poor coverage or poor scalability. In this work, we develop several new approaches for low overhead data race detection. Our approaches aim to guarantee high precision and accuracy of race checking while maintaining a low runtime and memory overhead. We present two race checkers for C/C++ OpenMP programs that target two different classes of programs. The first, ARCHER, is fast but requires large amount of memory, so it ideally targets applications that require only a small portion of the available on-node memory. On the other hand, SWORD strikes a balance between fast zero memory overhead data collection followed by offline analysis that can take a long time, but it often report most races quickly. Given that race checking was impossible for large OpenMP applications, our contributions are the best available advances in what is known to be a difficult NP-complete problem. We performed an extensive evaluation of the tools on existing OpenMP programs and HPC benchmarks. Results show that both tools guarantee to identify all the races of a program in a given run without reporting any false alarms. The tools are user-friendly, hence serve as an important instrument for the daily work of programmers to help them identify data races early during development and production testing. Furthermore, our demonstrated success on real-world applications puts these tools on the top list of debugging tools for scientists at large

Operational experience, improvements, and performance of the CDF Run II silicon vertex detector

The Collider Detector at Fermilab (CDF) pursues a broad physics program at Fermilab's Tevatron collider. Between Run II commissioning in early 2001 and the end of operations in September 2011, the Tevatron delivered 12 fb-1 of integrated luminosity of p-pbar collisions at sqrt(s)=1.96 TeV. Many physics analyses undertaken by CDF require heavy flavor tagging with large charged particle tracking acceptance. To realize these goals, in 2001 CDF installed eight layers of silicon microstrip detectors around its interaction region. These detectors were designed for 2--5 years of operation, radiation doses up to 2 Mrad (0.02 Gy), and were expected to be replaced in 2004. The sensors were not replaced, and the Tevatron run was extended for several years beyond its design, exposing the sensors and electronics to much higher radiation doses than anticipated. In this paper we describe the operational challenges encountered over the past 10 years of running the CDF silicon detectors, the preventive measures undertaken, and the improvements made along the way to ensure their optimal performance for collecting high quality physics data. In addition, we describe the quantities and methods used to monitor radiation damage in the sensors for optimal performance and summarize the detector performance quantities important to CDF's physics program, including vertex resolution, heavy flavor tagging, and silicon vertex trigger performance.Comment: Preprint accepted for publication in Nuclear Instruments and Methods A (07/31/2013