In the Spring 2023 semester, a group of Parallel and Distributed Programming (CSC 248/448) students showcased their remarkable research and implementations in a series of presentations. Their projects span a wide range of fields, from optimization algorithms to parallel computing frameworks. Here is some brief Information about their presentations.

1. Aayush Poudel: Ant Colony Optimization (ACO) for the Traveling Salesman Problem (TSP)
   • Aayush Poudel’s presentation revolved around the fascinating application of Ant Colony Optimization to solve the Traveling Salesman Problem.
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2. Matt Nappo: GPU Implementation of ACO for TSP In his presentation
   • By harnessing the parallel processing capabilities of GPUs, Matt demonstrated an efficient implementation of ACO for the Traveling Salesman Problem.
3. Yifan Zhu and Zeliang Zhang: Parallel ANN Framework in Rust
   • Yifan Zhu and Zeliang Zhang collaborated on a project that involved building a parallel Artificial Neural Network (ANN) framework using the Rust programming language. Their framework leveraged the inherent parallelism in neural networks, unlocking increased performance and scalability.
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4. Jiakun Fan: Implementing Software Transactional Memory using Rust
   • Jiakun Fan delved into concurrency control by implementing Software Transactional Memory (STM) using the Rust programming language. STM provides an alternative approach to traditional lock-based synchronization, allowing for simplified concurrent programming. Jiakun’s project showcased the feasibility of utilizing Rust’s unique features to build concurrent systems.
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5. Shaotong Sun and Jionghao Han: PLUSS Sampler Optimization
   • Shaotong Sun and Jionghao Han collaborated on a project to optimize the PLUSS sampler. Their work involved enhancing the performance and efficiency of the sampler through parallelization techniques.
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6. Yiming Leng: Survey Study of Parallel A*
   • Yiming Leng undertook a comprehensive survey study exploring the parallelization of the A* search algorithm. A* is widely used in pathfinding and optimization problems, and Yiming’s research focused on the potential benefits and challenges of parallelizing this popular algorithm.
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7. Ziqi Feng: Design and Evaluation of a Parallel SAT Solver
   • Ziqi Feng’s presentation concerned designing and evaluating a parallel SAT (Satisfiability) solver. SAT solvers play a crucial role in solving Boolean satisfiability problems, and Ziqi’s project aimed to enhance their performance by leveraging parallel computing techniques.
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8. Suumil Roy: Parallel Video Compression using MPI
   • Suumil Roy’s project focused on leveraging the Message Passing Interface (MPI) for parallel video compression. Video compression is crucial in various domains, including streaming and storage. By leveraging the power of parallel computing, Suumil demonstrated how MPI enables the efficient distribution of computational tasks across multiple processing units.
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9. Muhammad Qasim: A RAFT-based Key-Value Store Implementation
   • Muhammad Qasim’s presentation focused on implementing a distributed key-value store using the RAFT consensus algorithm. Key-value stores are fundamental data structures in distributed systems, and the RAFT consensus algorithm ensures fault tolerance and consistency among distributed nodes.
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10. Donovan Zhong: RAFT-based Key-Value Storage Implementation
    • Donovan Zhong’s project complemented Muhammad’s work by presenting another RAFT-based key-value storage implementation perspective. Donovan’s implementation provided insights into the challenges and intricacies of building fault-tolerant and distributed key-value storage systems.
11. Luchuan Song: Highly Parallel Tensor Computation for Classical Simulation of Quantum Circuits Using GPUs
    • Luchuan Song’s presentation unveiled an approach to parallel tensor computation for the classical simulation of quantum circuits. Quantum computing has the potential to revolutionize various industries, but its simulation on classical computers remains a challenging task. Luchuan’s project harnessed the power of Graphics Processing Units (GPUs) to accelerate tensor operations, allowing for efficient and scalable simulation of quantum circuits.
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12. Woody Wu and Will Nguyen: Parallel N-Body Simulation in Rust Programming Language
    • Working together as a team, Woody Wu and Will Nguyen tackled the intricate task of simulating N-body systems. N-body simulations involve modeling the interactions and movements of particles or celestial bodies, making them essential in various scientific domains. In collaboration, they presented their project using various parallel programming frameworks such as Rust Rayon, MPI, and OpenMP. By leveraging these powerful tools, they explored the realm of high-performance computing to achieve efficient and scalable simulations.
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The presentation slides can be found at https://github.com/dcompiler/258s23