23-07-2012, 03:09 PM
Introduction to Cluster Computing
Introduction toCluster Computing.ppt (Size: 944 KB / Downloads: 47)
High Performance Computing
CPU clock frequency
Parallel computers
Alternate technologies
Optical
Bio
Molecular
“Parallel” Computing
Traditional supercomputers
SIMD, MIMD, pipelines
Tightly coupled shared memory
Bus level connections
Expensive to buy and to maintain
Cooperating networks of computers
Traditional Supercomputers
Very high starting cost
Expensive hardware
Expensive software
High maintenance
Expensive to upgrade
Computational Grids
“Grids are persistent environments that enable software applications to integrate instruments, displays, computational and information resources that are managed by diverse organizations in widespread locations.”
“Workstation Operating System”
Authenticated users
Protection of resources
Multiple processes
Preemptive scheduling
Virtual Memory
Hierarchical file systems
Network centric
Distributed Programs
Spatially distributed programs
A part here, a part there, …
Parallel
Synergy
Temporally distributed programs
Finish the work of your “great grand father”
Compute half today, half tomorrow
Combine the results at the end
Migratory programs
Have computation, will travel
SPMD
Single program, multiple data
Contrast with SIMD
Same program runs on multiple nodes
May or may not be lock-step
Nodes may be of different speeds
Barrier synchronization
Distributed Shared Memory
“Simultaneous” read/write access by spatially distributed processors
Abstraction layer of an implementation built from message passing primitives
Semantics not so clean
Clusters of Workstations
Inexpensive alternative to traditional supercomputers
High availability
Lower down time
Easier access
Development platform with production runs on traditional supercomputers
Cluster Characteristics
Commodity off the shelf hardware
Networked
Common Home Directories
Open source software and OS
Support message passing programming
Batch scheduling of jobs
Process migration
Why are Linux Clusters Good?
Low initial implementation cost
Inexpensive PCs
Standard components and Networks
Free Software: Linux, GNU, MPI, PVM
Scalability: can grow and shrink
Familiar technology, easy for user to adopt the approach, use and maintain system.
Introduction toCluster Computing.ppt (Size: 944 KB / Downloads: 47)
High Performance Computing
CPU clock frequency
Parallel computers
Alternate technologies
Optical
Bio
Molecular
“Parallel” Computing
Traditional supercomputers
SIMD, MIMD, pipelines
Tightly coupled shared memory
Bus level connections
Expensive to buy and to maintain
Cooperating networks of computers
Traditional Supercomputers
Very high starting cost
Expensive hardware
Expensive software
High maintenance
Expensive to upgrade
Computational Grids
“Grids are persistent environments that enable software applications to integrate instruments, displays, computational and information resources that are managed by diverse organizations in widespread locations.”
“Workstation Operating System”
Authenticated users
Protection of resources
Multiple processes
Preemptive scheduling
Virtual Memory
Hierarchical file systems
Network centric
Distributed Programs
Spatially distributed programs
A part here, a part there, …
Parallel
Synergy
Temporally distributed programs
Finish the work of your “great grand father”
Compute half today, half tomorrow
Combine the results at the end
Migratory programs
Have computation, will travel
SPMD
Single program, multiple data
Contrast with SIMD
Same program runs on multiple nodes
May or may not be lock-step
Nodes may be of different speeds
Barrier synchronization
Distributed Shared Memory
“Simultaneous” read/write access by spatially distributed processors
Abstraction layer of an implementation built from message passing primitives
Semantics not so clean
Clusters of Workstations
Inexpensive alternative to traditional supercomputers
High availability
Lower down time
Easier access
Development platform with production runs on traditional supercomputers
Cluster Characteristics
Commodity off the shelf hardware
Networked
Common Home Directories
Open source software and OS
Support message passing programming
Batch scheduling of jobs
Process migration
Why are Linux Clusters Good?
Low initial implementation cost
Inexpensive PCs
Standard components and Networks
Free Software: Linux, GNU, MPI, PVM
Scalability: can grow and shrink
Familiar technology, easy for user to adopt the approach, use and maintain system.