HiPC 2016 will feature the 9th Student Research Symposium on High Performance Computing, Data, and Analytics aimed at stimulating and fostering student research, and providing an international forum to highlight student research accomplishments in HPC. The symposium will also give students exposure to the best practices of senior HPC researchers in academia and industry. In a departure from previous years, the symposium will feature only student posters; there will be no talks by students. This change will give students a greater opportunity for other enriching experiences. As well as hearing keynote talks and attending technical sessions, SRS students will be able to attend and participate in workshops and take advantage of information provided by industry exhibits, demos, and presentations. The Conference Reception and multiple Student Symposium Poster Exhibit sessions will provide an opportunity for students to interact with HPC researchers and practitioners (and recruiters) from academia and industry.
Papers Accepted for Poster Presentation
The following papers have been accepted for poster presentation on both Day 2 and Day 3 of the conference. They will be on display next to the conference industry exhibits, and available for viewing throughout both days. Student authors will be available during breaks to answer questions.
D-face: Parallel implementation of CNN based Face Classifier using Drone Data on K40 & Jetson TK1.
Multiscale multiphysics process on a HPC infrastructure: Application to coral growth process.
Parallelization of Depth-First Traversal using Efficient Load Balancing.
Performance Improvement for Multi-Key Quick Sort using Kepler GPUs.
Simultaneous Solving of Linear Programming Problems in GPU.
Answering "If-What" Questions to Manage Batch Systems.
A Robust and Secure Cloud-Based RFID Authentication Protocol.
Balancing Energy with Routing Strategies using Secure Routing Protocol in Wireless Sensor Networks.
Scalability Analysis of Neural Networks and Extreme Learning Machines on Multi-Core Systems.
Scaling up Training of Deep BLSTM Networks for Handwritten Text Recognition.
SWIFT: A Fast Enhanced String Matching Algorithm for Heterogeneous Architectures.