24 monthsProgramme duration
13,315 USDTuition Fee/year
Programme overview
Main Subject
Computer Science and Information Systems
Degree
Other
Study Level
Masters
Study Mode
Blended
Cloud and high performance computing is one of the fastest growing computing areas, whose job market is growing rapidly across the globe. This interdisciplinary program is designed to equip students with the needed knowledge to design, build, operate, use, and maintain large systems designed for hosting cloud applications or running compute-intensive tasks for simulation and other purposes, as well as small computing modules designed to operate the edge of the network. This skill is highly demanded in numerous sectors: energy, healthcare, telecommunications, big data, e-commerce, defense, etc.
This program provides in-depth coverage of modern computing architectures and programming paradigms. Topics include parallel process architectures (parallel and pipelined dataflow, vectorization methods, interconnection, etc.), GPU architecture (functional units, CUDA, memory access coalescing, shared memory usage, etc.), distributed computing (threads, and abstraction, mutual exclusion, condition variables, atomic instructions, etc.), and quantum computing (qubits, entanglement, quantum protocols, quantum machine learning, etc.). The program also covers cloud and edge computing (cloud services, RESTful APIs, XaaS pyramid, serverless computing, cloud big data processing, etc.), parallel algorithms (divide-and-conquer, parallel prefix, pointer jumping, list ranking, Euler's path, ear decomposition, parallel complexity etc.), and big data analytics (smart clouds, machine learning and visualization of big data, privacy, etc.). Essential knowledge of linear algebra is also included (e.g. direct methods for large sparse linear systems, LU factorizations, regularization of ill-conditioned least squares problems).
This program provides in-depth coverage of modern computing architectures and programming paradigms. Topics include parallel process architectures (parallel and pipelined dataflow, vectorization methods, interconnection, etc.), GPU architecture (functional units, CUDA, memory access coalescing, shared memory usage, etc.), distributed computing (threads, and abstraction, mutual exclusion, condition variables, atomic instructions, etc.), and quantum computing (qubits, entanglement, quantum protocols, quantum machine learning, etc.). The program also covers cloud and edge computing (cloud services, RESTful APIs, XaaS pyramid, serverless computing, cloud big data processing, etc.), parallel algorithms (divide-and-conquer, parallel prefix, pointer jumping, list ranking, Euler's path, ear decomposition, parallel complexity etc.), and big data analytics (smart clouds, machine learning and visualization of big data, privacy, etc.). Essential knowledge of linear algebra is also included (e.g. direct methods for large sparse linear systems, LU factorizations, regularization of ill-conditioned least squares problems).
Programme overview
Main Subject
Computer Science and Information Systems
Degree
Other
Study Level
Masters
Study Mode
Blended
Cloud and high performance computing is one of the fastest growing computing areas, whose job market is growing rapidly across the globe. This interdisciplinary program is designed to equip students with the needed knowledge to design, build, operate, use, and maintain large systems designed for hosting cloud applications or running compute-intensive tasks for simulation and other purposes, as well as small computing modules designed to operate the edge of the network. This skill is highly demanded in numerous sectors: energy, healthcare, telecommunications, big data, e-commerce, defense, etc.
This program provides in-depth coverage of modern computing architectures and programming paradigms. Topics include parallel process architectures (parallel and pipelined dataflow, vectorization methods, interconnection, etc.), GPU architecture (functional units, CUDA, memory access coalescing, shared memory usage, etc.), distributed computing (threads, and abstraction, mutual exclusion, condition variables, atomic instructions, etc.), and quantum computing (qubits, entanglement, quantum protocols, quantum machine learning, etc.). The program also covers cloud and edge computing (cloud services, RESTful APIs, XaaS pyramid, serverless computing, cloud big data processing, etc.), parallel algorithms (divide-and-conquer, parallel prefix, pointer jumping, list ranking, Euler's path, ear decomposition, parallel complexity etc.), and big data analytics (smart clouds, machine learning and visualization of big data, privacy, etc.). Essential knowledge of linear algebra is also included (e.g. direct methods for large sparse linear systems, LU factorizations, regularization of ill-conditioned least squares problems).
This program provides in-depth coverage of modern computing architectures and programming paradigms. Topics include parallel process architectures (parallel and pipelined dataflow, vectorization methods, interconnection, etc.), GPU architecture (functional units, CUDA, memory access coalescing, shared memory usage, etc.), distributed computing (threads, and abstraction, mutual exclusion, condition variables, atomic instructions, etc.), and quantum computing (qubits, entanglement, quantum protocols, quantum machine learning, etc.). The program also covers cloud and edge computing (cloud services, RESTful APIs, XaaS pyramid, serverless computing, cloud big data processing, etc.), parallel algorithms (divide-and-conquer, parallel prefix, pointer jumping, list ranking, Euler's path, ear decomposition, parallel complexity etc.), and big data analytics (smart clouds, machine learning and visualization of big data, privacy, etc.). Essential knowledge of linear algebra is also included (e.g. direct methods for large sparse linear systems, LU factorizations, regularization of ill-conditioned least squares problems).
Admission Requirements
105+
2.5+
6+
70+
All candidates for the MX in High Performance and Cloud Computing must satisfy the overall requirements of KFUPM in addition to the following:
a) The general requirements for the professional Master's are as follows:
Satisfying the minimum admission requirements does not guarantee admission into the program, as final admission is subject to the evaluation of the entire application, and a personal interview. Based on the assessment of the applicant's file and the personal interview, the admission committee might offer conditional acceptance for students who need to take deficiency courses.
The MX in High Performance and Cloud Computing consists of 9 core courses from different disciplines.
a) The general requirements for the professional Master's are as follows:
- A Grade-Point Average (GPA) of 2.5 or higher on a scale of 4.00
- Completion of TOFEL with a minimum score of 525 (PBT), 194 (CBT), or 70 (IBT). IELTS is also acceptable with a minimum of 6.0. (TOEFL is not required for students who graduated from KFUPM)
- At least two letters of recommendation
- A 4-year B.Sc. or equivalent degree in Computer Engineering, Computer Science, Software Engineering, or Electrical Engineering.
- Knowledge of Programming and data structure.
- Basics of Computer Organization and Architecture.
- Basics of Linear Algebra
Satisfying the minimum admission requirements does not guarantee admission into the program, as final admission is subject to the evaluation of the entire application, and a personal interview. Based on the assessment of the applicant's file and the personal interview, the admission committee might offer conditional acceptance for students who need to take deficiency courses.
The MX in High Performance and Cloud Computing consists of 9 core courses from different disciplines.
2 Years
- Candidates are required to submit references or letter(s) of recommendation for acceptance
- Candidates are required to submit an essay(s) for acceptance
Tuition fees
Domestic
13,315 USD
International
13,315 USD
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