Reify Your Collection Queries for Modularity and Speed!

Modularity and efficiency are often contradicting requirements, such that programers have to trade one for the other. We analyze this dilemma in the context of programs operating on collections. Performance-critical code using collections need often to be hand-optimized, leading to non-modular, brittle, and redundant code. In principle, this dilemma could be avoided by automatic collection-specific optimizations, such as fusion of collection traversals, usage of indexing, or reordering of filters. Unfortunately, it is not obvious how to encode such optimizations in terms of ordinary collection APIs, because the program operating on the collections is not reified and hence cannot be analyzed. We propose SQuOpt, the Scala Query Optimizer--a deep embedding of the Scala collections API that allows such analyses and optimizations to be defined and executed within Scala, without relying on external tools or compiler extensions. SQuOpt provides the same "look and feel" (syntax and static typing guarantees) as the standard collections API. We evaluate SQuOpt by re-implementing several code analyses of the Findbugs tool using SQuOpt, show average speedups of 12x with a maximum of 12800x and hence demonstrate that SQuOpt can reconcile modularity and efficiency in real-world applications.Comment: 20 page

Engineering a static verification tool for GPU kernels

We report on practical experiences over the last 2.5 years related to the engineering of GPUVerify, a static verification tool for OpenCL and CUDA GPU kernels, plotting the progress of GPUVerify from a prototype to a fully functional and relatively efficient analysis tool. Our hope is that this experience report will serve the verification community by helping to inform future tooling efforts. © 2014 Springer International Publishing

EASe : integrating search with learned episodes

Weak methods are insufficient to solve complex problems. Constrained weak methods, like hill-climbing, search too little of the problem space. Unconstrained weak methods, like breadth-first search, are intractable. Fortunately, through the integration of multiple weak methods more powerful problem solvers can be created. We demonstrate that augmenting a weak constrained search method with episodes provides a tractable method for solving a large class of problems. We demonstrate that these episodes can be generated using an unconstrained weak method while solving simple problems from a domain. We provide an analytical model of our approach and empirical results from the logic synthesis domain of VLSI design as well as the classic tile-sliding domain

Arbitrary boolean advertisements: the final step in supporting the boolean publish/subscribe model

Publish/subscribe systems allow for an efficient filtering of incoming information. This filtering is based on the specifications of subscriber interests, which are registered with the system as subscriptions. Publishers conversely specify advertisements, describing the messages they will send later on. What is missing so far is the support of arbitrary Boolean advertisements in publish/subscribe systems. Introducing the opportunity to specify these richer Boolean advertisements increases the accuracy of publishers to state their future messages compared to currently supported conjunctive advertisements. Thus, the amount of subscriptions forwarded in the network is reduced. Additionally, the system can more time efficiently decide whether a subscription needs to be forwarded and more space efficiently store and index advertisements. In this paper, we introduce a publish/subscribe system that supports arbitrary Boolean advertisements and, symmetrically, arbitrary Boolean subscriptions. We show the advantages of supporting arbitrary Boolean advertisements and present an algorithm to calculate the practically required overlapping relationship among subscriptions and advertisements. Additionally, we develop the first optimization approach for arbitrary Boolean advertisements, advertisement pruning. Advertisement pruning is tailored to optimize advertisements, which is a strong contrast to current optimizations for conjunctive advertisements. These recent proposals mainly apply subscription-based optimization ideas, which is leading to the same disadvantages. In the second part of this paper, our evaluation of practical experiments, we analyze the efficiency properties of our approach to determine the overlapping relationship. We also compare conjunctive solutions for the overlapping problem to our calculation algorithm to show its benefits. Finally, we present a detailed evaluation of the optimization potential of advertisement pruning. This includes the analysis of the effects of additionally optimizing subscriptions on the advertisement pruning optimization