MaxSAT Evaluation 2018 : New Developments and Detailed Results

Peer reviewe

Incomplete Pseudo-Boolean Optimization by Local Search Using a Decision Oracle

In many real-world problems, the task is to find an optimal solution within a finite set of solutions. Many of the problems, also known as combinatorial optimization problems, are NPhard. In other words, finding an optimal solution for the problems is computationally difficult. However, being important for many real-world applications, there is a demand for efficient ways to solve the problems. One approach is the declarative approach, where the problems are first encoded into a mathematical constraint language. Then, the encoded problem instance is solved by an algorithm developed for that constraint language. In this thesis, we focus on declarative pseudo-Boolean optimization (PBO). PBO is the set of integer programs (IP) where the variables can only be assigned to 0 or 1. For many real-world applications, finding an optimal solution is too time-consuming. Instead of finding an optimal solution, incomplete methods attempt to find good enough solutions in a given time limit. To the best of our knowledge, there are not many incomplete algorithms developed specifically for PBO. In this thesis, we adapt an incomplete method developed for the maximum satisfiability problem to PBO. In the adapted algorithm, which we call LS-ORACLE-PBO, a given PBO instance is solved using a form of local search that utilizes a pseudo-Boolean decision oracle when moving from one solution to another. We implement and empirically compare LS-ORACLE-PBO to another recent incomplete PBO algorithm called LS-PBO. The results show that, in general, our implementation is not competitive against LS-PBO. However, for some problem instances, our implementation provides better results than LS-PBO

MaxSAT Evaluation 2018 : Solver and Benchmark Descriptions

Non peer reviewe

MaxSAT Evaluation 2021 : Solver and Benchmark Descriptions

Non peer reviewe

MaxSAT Evaluation 2022 : Solver and Benchmark Descriptions

Non peer reviewe

MaxSAT Evaluation 2020 : Solver and Benchmark Descriptions

Non peer reviewe

Study of Fine-Grained, Irregular Parallel Applications on a Many-Core Processor

This dissertation demonstrates the possibility of obtaining strong speedups for a variety of parallel applications versus the best serial and parallel implementations on commodity platforms. These results were obtained using the PRAM-inspired Explicit Multi-Threading (XMT) many-core computing platform, which is designed to efficiently support execution of both serial and parallel code and switching between the two. Biconnectivity: For finding the biconnected components of a graph, we demonstrate speedups of 9x to 33x on XMT relative to the best serial algorithm using a relatively modest silicon budget. Further evidence suggests that speedups of 21x to 48x are possible. For graph connectivity, we demonstrate that XMT outperforms two contemporary NVIDIA GPUs of similar or greater silicon area. Prior studies of parallel biconnectivity algorithms achieved at most a 4x speedup, but we could not find biconnectivity code for GPUs to compare biconnectivity against them. Triconnectivity: We present a parallel solution to the problem of determining the triconnected components of an undirected graph. We obtain significant speedups on XMT over the only published optimal (linear-time) serial implementation of a triconnected components algorithm running on a modern CPU. To our knowledge, no other parallel implementation of a triconnected components algorithm has been published for any platform. Burrows-Wheeler compression: We present novel work-optimal parallel algorithms for Burrows-Wheeler compression and decompression of strings over a constant alphabet and their empirical evaluation. To validate these theoretical algorithms, we implement them on XMT and show speedups of up to 25x for compression, and 13x for decompression, versus bzip2, the de facto standard implementation of Burrows-Wheeler compression. Fast Fourier transform (FFT): Using FFT as an example, we examine the impact that adoption of some enabling technologies, including silicon photonics, would have on the performance of a many-core architecture. The results show that a single-chip many-core processor could potentially outperform a large high-performance computing cluster. Boosted decision trees: This chapter focuses on the hybrid memory architecture of the XMT computer platform, a key part of which is a flexible all-to-all interconnection network that connects processors to shared memory modules. First, to understand some recent advances in GPU memory architecture and how they relate to this hybrid memory architecture, we use microbenchmarks including list ranking. Then, we contrast the scalability of applications with that of routines. In particular, regardless of the scalability needs of full applications, some routines may involve smaller problem sizes, and in particular smaller levels of parallelism, perhaps even serial. To see how a hybrid memory architecture can benefit such applications, we simulate a computer with such an architecture and demonstrate the potential for a speedup of 3.3X over NVIDIA's most powerful GPU to date for XGBoost, an implementation of boosted decision trees, a timely machine learning approach. Boolean satisfiability (SAT): SAT is an important performance-hungry problem with applications in many problem domains. However, most work on parallelizing SAT solvers has focused on coarse-grained, mostly embarrassing parallelism. Here, we study fine-grained parallelism that can speed up existing sequential SAT solvers. We show the potential for speedups of up to 382X across a variety of problem instances. We hope that these results will stimulate future research