Distributed system replication is widely used as a means of fault-tolerance and scalability. However, it provides a spectrum of consistency choices that impose a dilemma for clients between correctness, responsiveness and availability. Given a sequential object and its integrity properties, we automatically synthesize a replicated object that guarantees state integrity and convergence and avoids unnecessary coordination. Our approach is based on a novel sufficient condition for integrity and convergence called well-coordination that requires certain orders between conflicting and dependent operations. We statically analyze the given sequential object to decide its conflicting and dependent methods and use this information to avoid coordination. We present novel coordination protocols that are parametric in terms of the analysis results and provide the well-coordination requirements. We implemented a tool called Hamsaz that can automatically analyze the given object, instantiate the protocols and synthesize replicated objects. We have applied Hamsaz to a suite of use-cases and synthesized replicated objects that are significantly more responsive than the strongly consistent baseline. CCS Concepts: • Theory of computation → Invariants; Program analysis; • Software and its engineering → Distributed programming languages; Distributed systems organizing principles;
Replication is a common technique to build reliable and scalable systems. Traditional strong consistency maintains the same total order of operations across replicas. This total order is the source of multiple desirable consistency properties: integrity, convergence and recency. However, maintaining the total order has proven to inhibit availability and performance. Weaker notions exhibit responsiveness and scalability; however, they forfeit the total order and hence its favorable properties. This project revives these properties with as little coordination as possible. It presents a tool called that given a sequential object with the declaration of its integrity and recency requirements, automatically synthesizes a correct-by-construction replicated object that simultaneously guarantees the three properties. It features a relational object specification language and a syntax-directed analysis that infers optimum staleness bounds. Further, it defines coordination-avoidance conditions and the operational semantics of replicated systems that provably guarantees the three properties. It characterizes the computational power and presents a protocol for recency-aware objects. uses automatic solvers statically and embeds them in the runtime to dynamically decide the validity of coordination-avoidance conditions. The experiments show that recency-aware objects reduce coordination and response time.
Software systems often use specialized combinations of data structures to store and retrieve data. Designing and maintaining custom data structures particularly concurrent ones is time-consuming and error-prone. We let the user declare the required data as a high-level specification of a relation and method interface, and automatically synthesize correct and efficient concurrent data representations. We present provably sound syntactic derivations to synthesize structures that efficiently support the interface. We then synthesize synchronization to support concurrent execution on the structures. Multiple candidate representations may satisfy the same specification and we aim at quantitative selection of the most efficient candidate. Previous works have either used dynamic auto-tuners to execute and measure the performance of the candidates or used static cost functions to estimate their performance. However, repeating the execution for many candidates is time-consuming and a single performance model cannot be an effective predictor of all workloads across all platforms. We present a novel approach to quantitative synthesis that learns the performance model. We developed a synthesis tool called Leqsy that trains an artificial neural network to statically predict the performance of candidate representations. Experimental evaluations demonstrate that Leqsy can synthesize near-optimum representations. CCS Concepts: • Software and its engineering → Concurrent programming structures.
Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics error-prone and time-consuming. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms handwritten code, and that fusion accelerates execution.
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