International Journal of Parallel Programming

Abstract Wireless sensor networks (WSNs) consist of sensor nodes that detect, process, and transmit various types of information to a base station unit. The development of energy-efficient routing protocols is a crucial challenge in WSNs. This study proposes a novel algorithm called RMOWOA, i.e., Revamped Multi-Objective Whale Optimization Algorithm, which utilizes concentric circles with different radii to partition the network. The circles are divided into eight equal sectors, and sections are formed at the intersections of sectors and layers. Each section contains a small number of nodes, and an agent is selected based on specific criteria. The nodes within each section transmit their detected information to the corresponding agent or cluster head. This process is repeated until the base station receives the data. The selection of agents is based on a WOA-based approach, known for enhancing the network's lifetime. The selected agent aggregates the data, performs redundant residue number-based error detection and rectification, and forwards the information to the lower segment's agent within that sector. The proposed RMOWOA algorithm is evaluated through simulation analysis and compared with established benchmark cluster head selection schemes such as SFA- Cluster Head Selection, FCGWO-Cluster Head Selection, and ABC-Cluster Head Selection. The experimental results of the RMOWOA algorithm demonstrate reduced energy consumption and extended network lifespan by effectively balancing the ratio of alive and dead nodes in WSNs.

Abstract Locality-based migration strategies are widely used in existing memory space management. Such type of strategies are consistently confronts with challenges in efficiently managing pages migration within constrained memory space, especially when new architecture such as hybrid of DRAM and NVM are emerging. Here we propose TransMigrator, an innovative predictive page migration model based on transformer architecture, which obtains a qualitative leap in the breadth and accuracy of prediction compared with traditional local-based methods. TransMigrator utilizes an end-to-end neural network to learn memory access behavior and page migration record in the long-term history and predict the most likely next page to fetch. Furthermore, a migration-management mechanism is designed to support the page-feeding from predictor, which in another way enhance the model robustness. The model achieves an average prediction accuracy better than 0.72, and saves an average of 0.24 access time overhead compared to strategies such as AC-CLOCK, THMigrator, and VC-HMM.

Abstract The Keccak algorithm plays a significant role in ensuring the security and confidentiality of data in modern information systems. However, it involves computational complexities that can hinder high-performance applications. This paper proposes a novel high-performance hardware architecture for the Keccak algorithm to address this problem. Our proposed hardware architecture exploits existing parallelisms in the Keccak algorithm to optimize its execution in terms of both speed and resource efficiency. By thoroughly analyzing the Keccak algorithm's structure and building blocks, we adapted our hardware architecture to take full advantage of the capabilities of modern FPGAs and ASICs. Key features of the high-performance hardware architecture include parallelized computation blocks, efficient digital design and a streamlined data path. In addition to these, we also make use of hardware level design considerations such as FPGA floorplanning, pipelining and bit-level parallelisms to increase the performance of our design. All these design considerations contribute to significantly increased processing speeds surpassing traditional software-based approaches and previous hardware-based implementations. Our design also minimizes resource usage, making it applicable to a wide variety of embedded and cryptographic systems. This makes our design suitable for applications that require both high throughput and secure data processing.

Abstract Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and vertex additions, as well as, deletions. Typical examples of dynamic graphs are social networks, collaboration networks, and road networks. Applying static algorithms repeatedly on dynamic graphs is inefficient. Further, due to the rapid growth of unstructured and semi-structured data, graph algorithms demand efficient parallel processing. Unfortunately, we know only a little about how to efficiently process dynamic graphs on massively parallel architectures such as GPUs. Existing approaches to represent and process dynamic graphs are either not general or are inefficient. In this work, we propose a graph library for dynamic graph algorithms over a GPU-tailored graph representation and exploits the warp-cooperative work-sharing execution model. The library, named Meerkat, builds upon a recently proposed dynamic graph representation on GPUs. This representation exploits a hashtable-based mechanism to store a vertex’s neighborhood. Meerkat also enables fast iteration through a group of vertices, a pattern common and crucial for achieving performance in graph applications. Our framework supports dynamic edge additions and edge deletions, along with their batched versions. Based on the efficient iterative patterns encoded in Meerkat, we implement dynamic versions of popular graph algorithms such as breadth-first search, single-source shortest paths, triangle counting, PageRank, and weakly connected components. We evaluated our implementations over the ones in other publicly available dynamic graph data structures and frameworks: GPMA, Hornet, and faimGraph. Using a variety of real-world graphs, we observe that Meerkat significantly improves the efficiency of the underlying dynamic graph algorithm, outperforming these frameworks.