Non-deterministic Thread Research Articles

ConcSpectre: Be Aware of Forthcoming Malware Hidden in Concurrent Programs

Concurrent programs with multiple threads executing in parallel are widely used to unleash the power of multicore computing systems. Owing to their complexity, a lot of research focuses on testing and debugging concurrent programs. Besides correctness, we find that security can also be compromised by concurrency. In this article, we present concurrent program spectre (ConcSpectre), a new security threat that hides malware in nondeterministic thread interleavings. To demonstrate such threat, we have developed a stealth malware technique called concurrent logic bomb by partitioning a piece of malicious code and injecting its components separately into a concurrent program. The malicious behavior can be triggered by certain thread interleavings that rarely happen (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; $</tex-math></inline-formula> 1%) under a normal execution environment. However, with a new technique called controllable probabilistic activation, we can activate such ConcSpectre malware with a very high probability (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 90%) by remotely disturbing thread scheduling. In the evaluation, more than 1000 ConcSpectre samples are generated, which bypassed most of the antivirus engines in VirusTotal and four well-known online dynamic malware analysis systems. We also demonstrate how to remotely trigger a ConcSpectre sample on a web server and control its activation probability. Our work shows an urgent need for new malware analysis methods for concurrent programs.

Plagiarism Detection of Multi-threaded Programs Using Frequent Behavioral Pattern Mining

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage the Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program’s behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multi-threaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth copes better with multi-threaded plagiarism detection than alternative approaches. Compared against the dynamic birthmark System Call Short Sequence Birthmark (SCSSB), FPBirth achieves 12.4%, 4.1% and 7.9% performance improvements with respect to union of resilience and credibility (URC), F-Measure and matthews correlation coefficient (MCC) metric, respectively.

On Scheduling Constraint Abstraction for Multi-Threaded Program Verification

Bounded model checking is among the most efficient techniques for the automated verification of concurrent programs. However, due to the nondeterministic thread interleavings, a large and complex formula is usually required to give an exact encoding of all possible behaviors, which significantly limits the scalability. Observing that the large formula is usually dominated by the exact encoding of the scheduling constraint, this paper proposes a novel scheduling constraint based abstraction refinement method for multi-threaded C program verification. Our method is both efficient in practice and complete in theory, which is challenging for existing techniques. To achieve this, we first proposed an effective and powerful technique which works well for nearly all benchmarks we evaluated. We have proposed the notion of Event Order Graph (EOG), and have devised two graph-based algorithms over EOG for counterexample validation and refinement generation, which can often obtain a small yet effective refinement constraint. Then, to ensure completeness, our method was enhanced with two constraint-based algorithms for counterexample validation and refinement generation. Experimental results on SV-COMP 2017 benchmarks and two real-world server systems indicate that our method is promising and significantly outperforms the state-of-the-art tools.

Plagiarism Detection of Multi-Threaded Programs via Siamese Neural Networks

Widespread intentional or unintentional software plagiarisms have posed serious threats to the healthy development of software industry. In order to detect such evolving software plagiarism, software dynamic birthmark techniques of better anti-obfuscation ability serve as one of the most promising methods. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, existing dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. Some thread-aware birthmarking methods have been then proposed to address this issue, which nevertheless largely rely on manual feature engineering and empirical observations without any ground-truth training, and thus require domain knowledge, making them inflexible to be deployed in the wild. Inspired by the success of self-guided optimization using deep neural networks and their superior feature learning ability, in this article, we transform multiple execution traces for each multi-threaded program under a specified input to the plain feature matrix, and feed it to the deep learning framework to learn latent representation as thread-aware birthmark that enjoys better semantic richness and perturbation resistance; instead of empirically determining the plagiarism over direct birthmark similarity metric, we further build up sophisticated siamese neural networks to supervise birthmark construction, similarity measurement, and decision making. Integrating our proposed method, a system called NeurMPD is developed to perform Neural network-based Multi-threaded program Plagiarism Detection. The experimental results based on a public software plagiarism sample set demonstrate that NeurMPD copes better with multi-threaded plagiarism detection than alternative approaches.

CODA

To exploit parallelism and scalability of multiple GPUs in a system, it is critical to place compute and data together. However, two key techniques that have been used to hide memory latency and improve thread-level parallelism (TLP), memory interleaving, and thread block scheduling, in traditional GPU systems are at odds with efficient use of multiple GPUs. Distributing data across multiple GPUs to improve overall memory bandwidth utilization incurs high remote traffic when the data and compute are misaligned. Nondeterministic thread block scheduling to improve compute resource utilization impedes co-placement of compute and data. Our goal in this work is to enable co-placement of compute and data in the presence of fine-grained interleaved memory with a low-cost approach. To this end, we propose a mechanism that identifies exclusively accessed data and place the data along with the thread block that accesses it in the same GPU. The key ideas are (1) the amount of data exclusively used by a thread block can be estimated, and that exclusive data (of any size) can be localized to one GPU with coarse-grained interleaved pages; (2) using the affinity-based thread block scheduling policy, we can co-place compute and data together; and (3) by using dual address mode with lightweight changes to virtual to physical page mappings, we can selectively choose different interleaved memory pages for each data structure. Our evaluations across a wide range of workloads show that the proposed mechanism improves performance by 31% and reduces 38% remote traffic over a baseline system.

Linux Kernel Data Races in Recent 5 Years

Data races are increasingly seen as concurrency bugs and they are difficult to reproduce and diagnose in parallel programs. Linux kernel is a large-scale software system, in which intensive thread-level parallelism and non-deterministic thread interleaving are more prone to race conditions. This paper conducts an investigation of real Linux kernel data races in recent 5 years. Our results show that there are about 500 real kernel data races reported and fixed in recent 5 years. File systems and drivers among all modules hold a much higher percentage of race conditions than other modules. We also conduct a case-bycase study on data races and graphically show how these data races are triggered with specific thread interleaving. Our analysis results are of interest to researchers and engineers who are committed to kernel data race detection and kernel development.

Model checking of concurrent programs with static analysis of field accesses

Systematic exploration of all possible thread interleavings is a popular approach to detect errors in multi-threaded programs. A common strategy is to use a partial order reduction technique and perform a non-deterministic thread scheduling choice only when the next instruction to be executed may possibly read or modify the global state. However, some verification frameworks and software model checkers, including Java Pathfinder (JPF), construct the program state space on-the-fly during traversal. The partial order reduction technique built into such a tool can use only the information available in the current state to determine whether the execution of a given instruction is globally-relevant. For example, the reduction technique has to make a thread choice at every field access on a heap object reachable from multiple threads, even in the case of fields that are really accessed only by a single thread during program execution, because it does not have any information about what may happen in the future after a particular state. These conservative decisions cause many redundant thread choices.We propose static analyses that identify globally-relevant field accesses more precisely. For each program state, the analyses give information about field accesses that may occur in the future after the given state. The state space traversal algorithm can use this information to soundly avoid creating unnecessary thread choices, and thus to reduce the number of thread interleavings that must be explored to cover all distinct behaviors of the given program. We implemented the proposed analyses using WALA and integrated them with JPF. Results of experiments on several Java programs show that the static analyses greatly improve the performance and scalability of JPF. In particular, it is now possible to check more complex programs than before in reasonable time.

Execution privatization for scheduler-oblivious concurrent programs

Making multithreaded execution less non-deterministic is a promising solution to address the difficulty of concurrent programming plagued by the non-deterministic thread scheduling. In fact, a vast category of concurrent programs are scheduler-oblivious: their execution is deterministic, regardless of the scheduling behavior. We present and formally prove a fundamental observation of the privatizability property for scheduler-oblivious programs, that paves the theoretical foundation for privatizing shared data accesses on a path segment. With privatization, the non-deterministic thread interleavings on the privatized accesses are isolated and as the consequence many concurrency problems are alleviated. We further present a path and context sensitive privatization algorithm that safely privatizes the program without introducing any additional program behavior. Our evaluation results show that the privatization opportunity pervasively exists in real world large complex concurrent systems. Through privatization, several real concurrency bugs are fixed and notable performance improvements are also achieved on benchmarks.

Asserting and checking determinism for multithreaded programs

The trend towards processors with more and more parallel cores is increasing the need for software that can take advantage of parallelism. The most widespread method for writing parallel software is to use explicit threads. Writing correct multithreaded programs, however, has proven to be quite challenging in practice. The key difficulty is nondeterminism. The threads of a parallel application may be interleaved nondeterministically during execution. In a buggy program, nondeterministic scheduling can lead to nondeterministic results—where some interleavings produce the correct result while others do not. We propose an assertion framework for specifying that regions of a parallel program behave deterministically despite nondeterministic thread interleaving. Our framework allows programmers to write assertions involving pairs of program states arisingfrom different parallel schedules. We describe an implementation of our deterministic assertions as a library for Java, and evaluate the utility of our specifications on a number of parallel Java benchmarks. We found specifying deterministic behavior to be quite simple using our assertions. Further, in experiments with our assertions, we were able to identify two races as true parallelism errors that lead to incorrect non-deterministic behavior. These races were distinguished from a number of benign races in the benchmarks.