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Finding and reproducing bugs in the presence of this nondeterminism has been the subject of much prior work in three main areas: 1 controlled concurrency-testing, where a custom scheduler replaces the OS scheduler to find subtle bugs; 2 record and replay, where sources of nondeterminism are captured and logged so that a failing execution can be replayed for debugging purposes; and 3 dynamic analysis for the detection of data races.

Our novel twist on record and replay is a sparse approach, where the sources of nondeterminism to record can be configured per application. This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse matrix codes and evaluates its performance in the context of wavefront parallellism.

Sparse computations incorporate indirect memory accesses such as x[col[j]] whose memory locations cannot be determined until runtime. The key contributions of this paper are two compile-time techniques for significantly reducing the overhead of runtime dependence testing: 1 identifying new equality constraints that result in more efficient runtime inspectors, and 2 identifying subset relations between dependence constraints such that one dependence test subsumes another one that is therefore eliminated. New equality constraints discovery is enabled by taking advantage of domain-specific knowledge about index arrays, such as col[j].

These simplifications lead to automatically-generated inspectors that make it practical to parallelize such computations. We analyze our simplification methods for a collection of seven sparse computations.

Comparative Metric Semantics of Programming Languages : Nondeterminism and Recursion

The evaluation shows our methods reduce the complexity of the runtime inspectors significantly. Experimental results for a collection of five large matrices show parallel speedups ranging from 2x to more than 8x running on a 8-core CPU. We propose a methodology for automatic generation of divide-and-conquer parallel implementations of sequential nested loops. We focus on a class of loops that traverse read-only multidimensional collections lists or arrays and compute a function over these collections.

Our approach is modular, in that, the inner loop nest is abstracted away to produce a simpler loop nest for parallelization. The summarized version of the loop nest is then parallelized. We present theoretical results to justify the correctness of our modular approach, and algorithmic solutions for automation.

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Experimental results demonstrate that our approach can parallelize highly non-trivial loop nests efficiently. Irregular data structures, as exemplified with sparse matrices, have proved to be essential in modern computing. Numerous sparse formats have been investigated to improve the overall performance of Sparse Matrix-Vector multiply SpMV. But in this work we propose instead to take a fundamentally different approach: to automatically build sets of regular sub-computations by mining for regular sub-regions in the irregular data structure.

Our approach leads to code that is specialized to the sparsity structure of the input matrix, but which does not need anymore any indirection array, thereby improving SIMD vectorizability. The universal composability UC framework is the established standard for analyzing cryptographic protocols in a modular way, such that security is preserved under concurrent composition with arbitrary other protocols.

However, although UC is widely used for on-paper proofs, prior attempts at systemizing it have fallen short, either by using a symbolic model thereby ruling out computational reduction proofs , or by limiting its expressiveness. In this paper, we lay the groundwork for building a concrete, executable implementation of the UC framework. Recent work on formal verification of differential privacy shows a trend toward usability and expressiveness -- generating a correctness proof of sophisticated algorithm while minimizing the annotation burden on programmers. Sometimes, combining those two requires substantial changes to program logics: one recent paper is able to verify Report Noisy Max automatically, but it involves a complex verification system using customized program logics and verifiers.

In this paper, we propose a new proof technique, called shadow execution, and embed it into a language called ShadowDP. ShadowDP uses shadow execution to generate proofs of differential privacy with very few programmer annotations and without relying on customized logics and verifiers. In addition to verifying Report Noisy Max, we show that it can verify a new variant of Sparse Vector that reports the gap between some noisy query answers and the noisy threshold.

Moreover, ShadowDP reduces the complexity of verification: for all of the algorithms we have evaluated, type checking and verification in total takes at most 3 seconds, while prior work takes minutes on the same algorithms. Distributed architectures for efficient processing of streaming data are increasingly critical to modern information processing systems. The goal of this paper is to develop type-based programming abstractions that facilitate correct and efficient deployment of a logical specification of the desired computation on such architectures.

In the proposed model, each communication link has an associated type specifying tagged data items along with a dependency relation over tags that captures the logical partial ordering constraints over data items. The semantics of a distributed stream processing system is then a function from input data traces to output data traces, where a data trace is an equivalence class of sequences of data items induced by the dependency relation. This data-trace transduction model generalizes both acyclic synchronous data-flow and relational query processors, and can specify computations over data streams with a rich variety of partial ordering and synchronization characteristics.

We then describe a set of programming templates for data-trace transductions: abstractions corresponding to common stream processing tasks. Our system automatically maps these high-level programs to a given topology on the distributed implementation platform Apache Storm while preserving the semantics.

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Our experimental evaluation shows that 1 while automatic parallelization deployed by existing systems may not preserve semantics, particularly when the computation is sensitive to the ordering of data items, our programming abstractions allow a natural specification of the query that contains a mix of ordering constraints while guaranteeing correct deployment, and 2 the throughput of the automatically compiled distributed code is comparable to that of hand-crafted distributed implementations. Despite the tremendous advances that have been made in the last decade on developing useful machine-learning applications, their wider adoption has been hindered by the lack of strong assurance guarantees that can be made about their behavior.

In this paper, we consider how formal verification techniques developed for traditional software systems can be repurposed for verification of reinforcement learning-enabled ones, a particularly important class of machine learning systems. Rather than enforcing safety by examining and altering the structure of a complex neural network implementation, our technique uses blackbox methods to synthesizes deterministic programs, simpler, more interpretable, approximations of the network that can nonetheless guarantee desired safety properties are preserved, even when the network is deployed in unanticipated or previously unobserved environments.

Our methodology frames the problem of neural network verification in terms of a counterexample and syntax-guided inductive synthesis procedure over these programs. The synthesis procedure searches for both a deterministic program and an inductive invariant over an infinite state transition system that represents a specification of an application's control logic.

Additional specifications defining environment-based constraints can also be provided to further refine the search space. Synthesized programs deployed in conjunction with a neural network implementation dynamically enforce safety conditions by monitoring and preventing potentially unsafe actions proposed by neural policies. Experimental results over a wide range of cyber-physical applications demonstrate that software-inspired formal verification techniques can be used to realize trustworthy reinforcement learning systems with low overhead.

Most traditional software systems are not built with the artificial intelligence support AI in mind. Among them, some may require human interventions to operate, e. We propose a novel framework called Autonomizer to autonomize these systems by installing the AI into the traditional programs.

Autonomizeris general so it can be applied to many real-world applications. We provide the primitives and the run-time support, where the primitives abstract common tasks of autonomization and the runtime support realizes them transparently. With the support of Autonomizer, the users can gain the AI support with little engineering efforts.

Like many other AI applications, the challenge lies in the feature selection, which we address by proposing multiple automated strategies based on the program analysis. Our experiment results on nine real-world applications show that the autonomization only requires adding a few lines to the source code. CNN pruning is an important method to adapt a large CNN model trained on general datasets to fit a more specialized task or a smaller device. The key challenge is on deciding which filters to remove in order to maximize the quality of the pruned networks while satisfying the constraints.

It is time-consuming due to the enormous configuration space and the slowness of CNN training. The problem has drawn many efforts from the machine learning field, which try to reduce the set of network configurations to explore. This work tackles the problem distinctively from a programming systems perspective, trying to speed up the evaluations of the remaining configurations through computation reuse via a compiler-based framework. We empirically uncover the existence of composability in the training of a collection of pruned CNN models, and point out the opportunities for computation reuse.

We then propose composability-based CNN pruning, and design a compression-based algorithm to efficiently identify the set of CNN layers to pre-train for maximizing their reuse benefits in CNN pruning. We further develop a compiler-based framework named Wootz, which, for an arbitrary CNN, automatically generates code that builds a Teacher-Student scheme to materialize composability-based pruning. Experiments show that network pruning enabled by Wootz shortens the state-of-art pruning process by up to X while producing significantly better pruning results.

In recent years, the notion of local robustness or robustness for short has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks.

Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search.


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We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Real world applications make heavy use of powerful libraries and frameworks, posing a significant challenge for static analysis as the library implementation may be very complex or unavailable. Thus, obtaining specifications that summarize the behaviors of the library is important as it enables static analyzers to precisely track the effects of APIs on the client program, without requiring the actual API implementation.

In this work, we propose a novel method for discovering aliasing specifications of APIs by learning from a large dataset of programs. Unlike prior work, our method does not require manual annotation, access to the library's source code or ability to run its APIs. Instead, it learns specifications in a fully unsupervised manner, by statically observing usages of APIs in the dataset.

The core idea is to learn a probabilistic model of interactions between API methods and aliasing objects, enabling identification of additional likely aliasing relations, and to then infer aliasing specifications of APIs that explain these relations. The learned specifications are then used to augment an API-aware points-to analysis. We implemented our approach in a tool called USpec and used it to automatically learn aliasing specifications from millions of source code files.

USpec learned over specifications of various Java and Python APIs, in the process improving the results of the points-to analysis and its clients. We present a new scalable, semi-supervised method for inferring taint analysis specifications by learning from a large dataset of programs.


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Taint specifications capture the role of library APIs source, sink, sanitizer and are a critical ingredient of any taint analyzer that aims to detect security violations based on information flow. The core idea of our method is to formulate the taint specification learning problem as a linear optimization task over a large set of information flow constraints.

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The resulting constraint system can then be efficiently solved with state-of-the-art solvers. Thanks to its scalability, our method can infer many new and interesting taint specifications by simultaneously learning from a large dataset of programs e. We implemented our method in an end-to-end system, called Seldon, targeting Python, a language where static specification inference is particularly hard due to lack of typing information. In this paper, we present a novel learning framework for inferring stateful preconditions i. We instantiate the learning framework with a specific learner and test generator to realize a precondition synthesis tool for C.

We use an extensive evaluation to show that the tool is highly effective in synthesizing preconditions for avoiding exceptions as well as synthesizing conditions under which methods commute. We introduce a new dynamic analysis technique to discover invariants in separation logic for heap-manipulating programs. First, we use a debugger to obtain rich program execution traces at locations of interest on sample inputs.

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These traces consist of heap and stack information of variables that point to dynamically allocated data structures. Next, we iteratively analyze separate memory regions related to each pointer variable and search for a formula over predefined heap predicates in separation logic to model these regions.

Finally, we combine the computed formulae into an invariant that describes the shape of explored memory regions. We present SLING, a tool that implements these ideas to automatically generate invariants in separation logic at arbitrary locations in C programs, e. Preliminary results on existing benchmarks show that SLING can efficiently generate correct and useful invariants for programs that manipulate a wide variety of complex data structures.

Analyzing the behavior of a program running on a processor that supports speculative execution is crucial for applications such as execution time estimation and side channel detection. Unfortunately, existing static analysis techniques based on abstract interpretation do not model speculative execution since they focus on functional properties of a program while speculative execution does not change the functionality.