Modern software development abstracts applications from the underlying infrastructure, enabling global-scale deployment with minimal concern about low-level networking details. However, when these infrastructure-agnostic software components need to communicate, they encounter significant networking limitations. This forces developers to either navigate complex, low-level networking constructs to achieve the desired connectivity or give up on truly flexible connectivity and limit their software to static connectivity patterns. In this paper, we focus on the evolving challenges of application connectivity in today's hyper-distributed reality. We propose to model connectivity around the notion of application services and have realized this proposal as ClusterLink, which exposes the app-level APIs for specifying communication policies at a very granular level and implements them efficiently. This paper shares details on ClusterLink design principles, APIs, architecture, and implementation, and shows that ClusterLink outperforms its closest competitor by 2.5x in throughput in a cloud-based experimental setting.

We present AS-CDG, a novel automatic scalable system for data-driven coverage-directed generation. The goal of AS-CDG is to find the test templates that maximize the probability of hitting uncovered events. The system contains two phases, one for a coarse-grained search that finds relevant parameters and the other for a fine-grained search for the settings of these parameters. To overcome the lack of evidence in the search, we replace the real target with an approximated target induced by neighboring events, for which we have evidence. Usage results on real-life units of high-end processors illustrate the ability of the proposed system to automatically find the desired test-templates and hit the previously uncovered target events.

We present Finding Related Interesting Events via Neighbor Detection (FRIENDS), a novel approach to assist verification teams with coverage closure. FRIENDS uses formal verification to find the neighboring events of a never-hit, or hard-to-hit, coverage event. The neighbor events of a given target event are defined as those events for which a test hitting them has higher probability of hitting the target event than a test not hitting them. Assuming that some of the neighboring events are easier to hit than the target event itself, this information can be used by the team or by a Coverage Directed Generation (CDG) tool during the coverage closure process.

We revisit the two main SAT-based algorithms for checking liveness properties of finite-state transition systems: the k-LIVENESS algorithm of [1] and the FAIR algorithm of [2]. These approaches are fundamentally different. k-LIVENESS works by translating the liveness property together with fairness constraints to the form F Gq, and then bounding the number of times the variable q can evaluate to false. FAIR works by finding an over-approximation R of reachable states, so that no state in R is contained on a fair cycle. Each technique has unique strengths on different problems. In this paper, we present a new algorithm k-FAIR that builds upon both techniques, synergistically leveraging their strengths. Experiments demonstrate that this combined approach is stronger than running both in parallel.

Post-silicon validation is a major challenge due to the combined effects of debug complexity and observability constraints. Assertions as well as a wide variety of checkers are used in pre-silicon stage to monitor certain functional scenarios. Pre-silicon checkers can be synthesized to coverage monitors in order to capture the coverage of certain events and improve the observability during post-silicon debug. Synthesizing thousands of coverage monitors can introduce unacceptable area and energy overhead. On the other hand, absence of coverage monitors would negatively impact post-silicon coverage analysis. In this paper, we propose a framework for cost-effective post-silicon coverage analysis by identifying hard-to-detect events coupled with trace-based coverage analysis. This paper makes three major contributions. We propose a method to utilize existing debug infrastructure to enable coverage analysis in the absence of synthesized coverage monitors. This analysis enables us to identify a small percentage of coverage monitors that need to be synthesized in order to provide a trade-off between observability and design overhead. To improve the observability further, we also present an observability-aware trace signal selection algorithm that gives priority to signals associated with important coverage monitors. Our experimental results demonstrate that an effective combination of coverage monitor selection and trace analysis can maintain the debugging observability with drastic reduction (up to 10 times) in the required coverage monitors.

This paper presents TAB-BackSpace, our novel scheme to provide the effect of an unlimited-length trace buffer with no on-chip overhead beyond the existing debug logic. We present the theoretical foundation of our work, simulation studies on how we reduce the possibility of computing an erroneous trace, and results from the bring-up lab on real silicon of an IBM POWER7 processor, where TAB-BackSpace computes almost a thousand additional cycles of trace buffer information without any additional on-chip overhead.

Dynamic BDD reordering is usually a computationally-demanding process, and may slow down BDD-based applications. We propose a novel algorithm for distributing this process over a number of computers, improving both reordering time and application time. Our algorithm is based on Rudell's popular sifting algorithm, and takes advantage of a few empirical observations we make regarding Rudell's algorithm. Experimental results show the efficiency and scalability of our approach, when applied within an industrial model checker.