The Muse approach to OR-parallel Prolog

Muse (Multi-sequential Prolog engines) is a simple and efficient approach to Or-parallel execution of Prolog programs. It is based on having several sequential Prolog engines, each with its local address space, and some shared memory space. It is currently implemented on a 7-processors machine with local/shared memory constructed at SICS, a 16-processors Sequent Symmetry, a 96-processors BBN Butterfly I, and a 45-processors BBN Butterfly II. The sequential SICStus Prolog system has been adapted to Or-parallel implementation. Extra overhead associated with this adaptation is very low in comparison with the other approaches. The speed-up factor is very close to the number of processors in the system for a large class of problems. The goal of this paper is to present the Muse execution model, some of its implementation issues, a variant of Prolog suitable for multiprocessor implementations, and some experimental results obtained from two different multiprocessor systems

Producing Scheduling that Causes Concurrent Programs to Fail

A noise maker is a tool that seeds a concurrent program with conditional synchronization primitives (such as yield()) for the purpose of increasing the likelihood that a bug manifest itself. This work explores the theory and practice of choosing where in the program to induce such thread switches at runtime. We introduce a novel fault model that classifies locations as .good., .neutral., or .bad,. based on the effect of a thread switch at the location. Using the model we explore the terms in which efficient search for real-life concurrent bugs can be carried out. We accordingly justify the use of probabilistic algorithms for this search and gain a deeper insight of the work done so far on noise-making. We validate our approach by experimenting with a set of programs taken from publicly available multi-threaded benchmark. Our empirical evidence demonstrates that real-life behavior is similar to what our model predicts

Faster Mutation Analysis via Equivalence Modulo States

Mutation analysis has many applications, such as asserting the quality of test suites and localizing faults. One important bottleneck of mutation analysis is scalability. The latest work explores the possibility of reducing the redundant execution via split-stream execution. However, split-stream execution is only able to remove redundant execution before the first mutated statement. In this paper we try to also reduce some of the redundant execution after the execution of the first mutated statement. We observe that, although many mutated statements are not equivalent, the execution result of those mutated statements may still be equivalent to the result of the original statement. In other words, the statements are equivalent modulo the current state. In this paper we propose a fast mutation analysis approach, AccMut. AccMut automatically detects the equivalence modulo states among a statement and its mutations, then groups the statements into equivalence classes modulo states, and uses only one process to represent each class. In this way, we can significantly reduce the number of split processes. Our experiments show that our approach can further accelerate mutation analysis on top of split-stream execution with a speedup of 2.56x on average.Comment: Submitted to conferenc

Design and evaluation of a genomics variant analysis pipeline using GATK Spark tools

Scalable and efficient processing of genome sequence data, i.e. for variant discovery, is key to the mainstream adoption of High Throughput technology for disease prevention and for clinical use. Achieving scalability, however, requires a significant effort to enable the parallel execution of the analysis tools that make up the pipelines. This is facilitated by the new Spark versions of the well-known GATK toolkit, which offer a black-box approach by transparently exploiting the underlying Map Reduce architecture. In this paper we report on our experience implementing a standard variant discovery pipeline using GATK 4.0 with Docker-based deployment over a cluster. We provide a preliminary performance analysis, comparing the processing times and cost to those of the new Microsoft Genomics Services

A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks

Among the many possible approaches for the parallelization of self-organizing networks, and in particular of growing self-organizing networks, perhaps the most common one is producing an optimized, parallel implementation of the standard sequential algorithms reported in the literature. In this paper we explore an alternative approach, based on a new algorithm variant specifically designed to match the features of the large-scale, fine-grained parallelism of GPUs, in which multiple input signals are processed at once. Comparative tests have been performed, using both parallel and sequential implementations of the new algorithm variant, in particular for a growing self-organizing network that reconstructs surfaces from point clouds. The experimental results show that this approach allows harnessing in a more effective way the intrinsic parallelism that the self-organizing networks algorithms seem intuitively to suggest, obtaining better performances even with networks of smaller size.Comment: 17 page

Percolation scheduling for non-VLIW machines

Percolation Scheduling, a technique for compile-time code parallelization, has proven very successful for exploiting fine-grain irregular parallelism in ordinary programs. Currently, this technology is targeted only to VLIW (Very Long Instruction Word) machines, which have the advantages of 'free' synchronization and communication. Shared memory multi-processors can simulate the execution characteristics of VLIW machines with the use of static barriers. Preliminary results show that Percolation Scheduling can be used with good results on this type of architecture by increasing the granularity from operation level to source statement level, removing any redundant synchronization, and providing an efficient implementation of multi-way jumps