4-0-0-4
MBICBI802: HIGH PERFORMANCE COMPUTING
Course objective: The course will equip the students with essential concepts of High
Performance Computing and the importance of Cloud Computing architectures in today’s world.
UNIT - I
15 periods
PARALLEL PROCESSING AND PIPELINING PROCESSING
Overview of parallel processing and pipelining processing, study and comparison of uniprocessors and parallel processors, conventional and EPIC architecture; Evolution of parallel
processors, future trends and there architecture.
Necessity of high performance, Constraints of conventional architecture, Parallelism in uniprocessor system, Evolution of parallel processors, future trends, Architectural Classification,
Applications of parallel processing, Instruction level Parallelism and Thread Level Parallelism,
Explicitly Parallel Instruction Computing (EPIC) Architecture, Case study of Intel Itanium
Processor. Principles of scalable performance: Performance Metrics and Measures, Speedup
Performance Laws. Programming aspects for Intel Itanium Processor
UNIT - II
15 periods
SIMD and ARRAY PROCESSORS
Parallel computers- Taxonomy of parallel computing paradigms SIMD Computer Organisation
Masking and Data network mechanism, Inter PE Communication, Interconnection networks of
SIMD, Static Vs Dynamic network, cube hyper cube and Mesh Interconnection network.
Parallel Algorithms for Array Processors: Matrix Multiplication. Sorting, SIMD computer
organisation; Implementation issues of Matrix multiplication and sorting on array processor and
their analysis, Distributed memory programming with MPI, Shared memory programming with
openMP
UNIT - III
15 periods
CLOUD COMPUTING AND VIRTUALIZATION TECHNOLOGY
Introduction to cloud computing, definition, architecture-characteristics, components, Cloud
computing platforms, Saas, Paas, Iaas and others, Google App Engine- Amazon Ec2- Microsoft
Azure, Cloud Deployment models are cloud types: community, hybrid, private, and public
clouds. Virtualization technology- virtual machine technology- virtual machines and elastic
computing- desktop virtualization and application streaming-automating infrastructure
management
UNIT - IV
15 periods
DATE AND FILE SYSTEMS IN CLOUD
Data in the cloud: Relational databases, Cloud file systems: GFS and Hadoop File System,
BigTable, HBase and Dynamo. Map-Reduce and extensions: Parallel computing, The mapReduce model, Parallel efficiency of Map-Reduce, Relational operations using Map-Reduce,
Features and comparisons among GFS, HDFS
REFERENCES
1. “Cloud Computing : A Practical Approach”, Anthony T. Velte Toby J. Velte, Robert
Elsenpeter, The McGraw-Hill Osborne, 2009, 1e.
2. “Cloud Computing Bible”, Barrie Sosinsky, John Wiley & Sons, 2011, 1e.
LEARNING OUTCOMES
UNIT – I
UNIT – II
UNIT – III
UNIT –IV
The learner will be able to understand the essentials of parallel processing in
computing architectures
The learner will be able to explore the parallel computing and its types and
various algorithms associated with it.
The learner will understand the importance of Cloud computing concepts and
the various deployment models and service oriented architectures
The learner will be able to gain the knowledge on Cloud computing file
systems and Map reduce algorithms for parallel computing