Introduction to HPC architectures: Shared and Distributed memory architectures, Multiprocessor Architecture. Parallel Processing Concepts; Levels and model of parallelism: instruction, transaction, task, thread, memory, function, data flow models, demand-driven computation; Parallel architectures: superscalar architectures, multi-core, multi-threaded, server and cloud; Fundamental design issues in HPC: Load balancing, scheduling, synchronization and resource management.

Operating systems for scalable HPC; Parallel languages and programming environments; OpenMP, Pthread, MPI, java, Cilk; Performance analysis of parallel algorithms; Fundamental

limitations in HPC: bandwidth, latency, and latency hiding techniques; Benchmarking HPC: scientific, engineering, commercial applications and workloads.

Scalable storage systems: RAID, SSD cache, SAS, SAN; HPC based on cluster, cloud, and grid computing: economic model, infrastructure, platform, computation as service; Accelerated HPC: architecture, programming and typical accelerated system with GPU, FPGA, Xeon Phi, Cell BE; Power-aware HPC Design: computing and communication, processing, memory design, interconnect design, power management; Advanced topics: peta scale computing; big data processing, optics in HPC, quantum computers. Case Studies on the parallel machine and HPC cluster using Pthread, OpenMP, MPI, Nvidia Cuda and Cilk.

Pre-Requisite(s): Computer Organization and Architecture or equivalent.
Course Type: Lab

Course Objectives

• To comprehend Parallel Processing Fundamentals and Programming
• To develop Proficiency in HPC Programming Tools
• To explore HPC Systems and Technologies for real world applications
• To evaluate and innovate in HPC Design trough Data Centre Environments

Course Outcomes

CO1: Understand and apply various levels of parallelism including instruction, transaction, task, thread, memory, function, and data flow models.
CO2: Implement and Optimize Parallel Programs with use of Modern Tools.
CO3: Analyze and compare scalable storage systems including RAID, SSD cache, SAS, and SAN.
CO4: Evaluate the potential and limitations of emerging HPC technologies and their applications in various domains

Evaluation Pattern: 70/30

Note: Continuous assessments can include quizzes, tutorials, lab assessments, case study and project reviews. Midterm and End semester exams can be a theory exam or lab integrated exam for two hours

1. George Hager, Gerhard Wellein – Introduction to High-Performance Computing for Scientists and Engineers, CRC Press, Taylor & Francis Group, 2019. Revised edition
2. Jason Sanders, Edward Kandrot – CUDA by Example: An Introduction to General Purpose GPU Programming 1st Edition.
3. Vipin Kumar , Ananth Grama , Anshul Gupta , George Karypis. Introduction to Parallel Computing (2nd ed.). Pearson India . 2003.
4. John L. Hennessy and David A. Patterson. Computer Architecture: A Quantitative Approach (5th ed.). Elsevier India Pvt. Ltd. 2011.
5. David B. Kirk and Wen-mei W. Hwu. Programming Massively Parallel Processors: A Hands-On Approach (1st ed.). Elsevier India Pvt. Ltd. 2010.
6. Michael T. Heath. Scientific Computing: An Introductory Survey (2nd ed.). McGraw Hill Education (India) Private Limited, 2011