LIPIcs

Union-Find (or Disjoint-Set Union) is one of the fundamental problems in computer science; it has been well-studied from both theoretical and practical perspectives in the sequential case. Recently, there has been mounting interest in analyzing this problem in the concurrent scenario, and several asymptotically-efficient algorithms have been proposed. Yet, to date, there is very little known about the practical performance of concurrent Union-Find. This work addresses this gap. We evaluate and analyze the performance of several concurrent Union-Find algorithms and optimization strategies across a wide range of platforms (Intel, AMD, and ARM) and workloads (social, random, and road networks, as well as integrations into more complex algorithms). We first observe that, due to the limited computational cost, the number of induced cache misses is the critical determining factor for the performance of existing algorithms. We introduce new techniques to reduce this cost by storing node priorities implicitly and by using plain reads and writes in a way that does not affect the correctness of the algorithms. Finally, we show that Union-Find implementations are an interesting application for Transactional Memory (TM): one of the fastest algorithm variants we discovered is a sequential one that uses coarse-grained locking with the lock elision optimization to reduce synchronization cost and increase scalability

HR Functions Productivity Boost by using AI

In today's fast-paced world, the Human Resources (HR) department plays a pivotal role in the success of any organization. With a plethora of tasks to manage, the HR team often faces the daunting challenge of screening and selecting the best candidates for various positions. To streamline this process, we propose a novel system that integrates the power of Kruskal algorithm for resume screening, conducting a qualifying test called "BTNT," and psychological testing like Emotional Intelligence to analyze the shortlisted candidates. Our proposed system utilizes a Knapsack approach under Dynamic Programming to suggest the most suitable candidates for HR roles. By automating these tedious HR tasks through Artificial Intelligence (AI), we ensure a faster, more accurate, and cost-effective selection process. Our research paper presents a detailed analysis of the proposed system's effectiveness and showcases the benefits of adopting this innovative approach. We believe that this cutting-edge system will revolutionize the HR industry by providing an efficient, objective, and unbiased selection process. The BTNT test's incorporation will help identify candidates' technical skills, while the psychological test will highlight their soft skills. This holistic approach ensures that organizations not only hire the best-fit candidates but also create a positive work environment that fosters growth and development. Our research paper is a must-read for any HR professional looking to optimize their recruitment process and gain a competitive edge in the market. With our proposed system's implementation, companies can attract and retain top talent, improve employee productivity, and ultimately increase their efficiency

Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph-Based Benchmark Suite (GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201