Academics / Graduate Study / MS Programs / SpecializationsHigh-Performance Computing
The enormous growth in artificial intelligence (AI) and Internet of Things (IoT) is fueling a growing demand for high-efficiency computing to perform real-time analysis on massive amounts of data. In many industries, large clusters of servers must work together in parallel to complete tasks faster.
High-performance computing (HPC) refers to the practice of aggregating computing resources into clusters that can analyze huge volumes of data in parallel, and process calculations at speeds far exceeding what is possible using traditional computing. This is crucial for today’s businesses, institutions, and researchers, who are turning to HPC to solve large, complex, performance-intensive problems in far less time, with higher accuracy, and at a lower cost than traditional methods. For example, medical researchers are using HPC to speed up time to develop vaccines, screen for diseases, and provide more accurate patient diagnoses. Financial institutions are using HPC to automate trading, analyze market trends, and detect credit card fraud. Media businesses are using HPC to stream live events, produce special effects, edit films, and create immersive entertainment. The ability to process large volumes of data at high speeds is prompting businesses in financial, energy, healthcare, retail, and manufacturing to adopt these HPC systems to solve some of the world’s biggest problems.
With the rapid growth of multi-core processors, GPUs, hardware accelerators, and networked computing platforms, HPC is becoming more ubiquitous and easily accessible. With cloud computing, HPC infrastructure can now be procured and deployed faster and at any given moment. In this track, you will gain the necessary skills in all aspects of high-performance computing to meet the needs of this rising industry, including parallel programming, computer architecture, and distributed computing.
Recommended Courses
Core Courses
Select at least six courses from the following list:
- ECE 303 Advanced Digital Design
- ECE 329 The Art of Multicore Concurrent Programming
- ECE 355 ASIC and FPGA Design
- ECE 358 Intro to Parallel Computing
- ECE 361 Computer Architecture I
- ECE 368, 468 Programming Massively Parallel Processors with CUDA
- ECE 387 Real-Time Digital Systems Design and Verification with FPGAs
- ECE 392 VLSI Systems Design Projects
- ECE 453 Parallel Architectures
- ECE 334 Fundamentals of Blockchains and Decentralization
- ECE 395, 495 Technology Infrastructure: Concepts, Requirements, Design and Operation
- ECE 424 Distributed Optimization
Elective Courses
Select up to six courses from the following list:
- ECE 362 Computer Architecture Project
- ECE 510 Social Media Mining
- CS 310 Scalable Software Architectures
- CS 321 Programming Languages
- CS 322 Compiler Construction
- CS 323 Code Analysis and Transformation
- CS 336 Design & Analysis of Algorithms
- CS 339 Intro to Databases
- CS 345 Distributed Systems
- CS 368, 468 Programming Massively Parallel Processors with CUDA
- ECE 326 Electronic System Design I
- ECE 327 Electronic System Design II
- ECE 335, 435 Deep Learning Foundations from Scratch
- ECE 375, 475 Machine Learning: Foundations, Applications, and Algorithms
- ECE 395, 495 Introduction to Smart Grid Systems
- ECE 424 Distributed Optimization
- ECE 435 Deep Learning Foundations from Scratch
- ECE 510 Topics in Wireless Communications and Networking
More in this section
- Artificial Intelligence and Machine Learning
- Computer Vision and Image Processing
- Network and Communication Systems
- Quantum Computing, Sensing & Communications
- Robotics and Autonomous Systems
- Specializations
- Cybersecurity
- Photonics & Optoelectronics
- Embedded Systems
- Internet-of-Things & Edge Computing
- Semiconductors
- Sustainable Energy and Low-Power Design