Cluster..
Content
management of groups of systems and their applications. The goal of cluster computing is to facilitate sharing a computer load over several systems without either the users of system or the administrators needing to know that more than one system is involved. The Windows NT Server Edition of the Windows operating system is an example of a base operating system that has been modified to include architecture that facilitates a cluster computing environment to be established. Cluster computing has been employed for over fifteen years but it is the recent demand for higher availability in small businesses that has caused an explosion in this field. Electronic databases and electronic malls have become essential to the daily operation of small businesses. Access to this critical information by these entities has created a large demand for cluster computing principle features.
21
There are some key concepts that must be understood when forming a cluster computing resource. Nodes or systems are the individual members of a cluster. They can be computers, servers, and other such hardware although each node generally has memory and processing capabilities. If one node becomes unavailable the other nodes can carry the demand load so that applications or services are always available. There must be at least two nodes to compose a cluster structure otherwise they are just called servers. The collection of software on each node that manages all cluster specific activity is called the cluster service. The cluster service manages all of the resources, the canonical items in the system, and sees then as identical opaque objects. Resources can be such things as
physical hardware devices, like disk drives and network cards, logical items, like logical disk volumes, TCP/IP addresses, applications, and databases.
22
When a resource is providing its service on a specific node it is said to be on-line. A collection of resources to be managed as a single unit is called a group. Groups contain all of the resources necessary to run a specific application, and if need be, to connect to the service provided by the application in the case of client systems. These groups allow administrators to combine resources into larger logical units so that they can be managed as a unit. This, of course, means that all operations performed on a group affect all resources contained within that group. Normally the development of a cluster computing system occurs in phases. The first phase involves establishing the underpinnings into the base operating system and building the foundation of the cluster components. These things should focus on providing enhanced availability to key applications using storage that is accessible to two nodes. The following stages occur as the demand increases and should allow for much larger clusters to be formed. These larger clusters should have a true distribution of applications, higher performance interconnects, widely distributed storage for easy accessibility and load balancing. Cluster computing will become even more prevalent in the future because of the growing needs and demands of businesses as well as the spread of the Internet.
Clustering Concepts
Clusters are in fact quite simple. They are a bunch of computers tied together with a network working on a large problem that has been broken down into smaller pieces. There are a number of different strategies we can use to tie them together. There are also a number of different software packages that can be used to make the software side of things work.
23
Parallelism The name of the game in high performance computing is parallelism. It is the quality that allows something to be done in parts that work independently rather than a task that has so many interlocking dependencies that it cannot be further broken down. Parallelism operates at two levels: hardware parallelism and software parallelism. Hardware Parallelism On one level hardware parallelism deals with the CPU of an individual system and how we can squeeze performance out of sub-components of the CPU that can speed up our code. At another level there is the parallelism that is gained by having multiple systems working on a computational problem in a distributed fashion. These systems are known as ‘fine grained’ for parallelism inside the CPU or having to do with the multiple CPUs in the same system, or ‘coarse grained’ for parallelism of a collection of separate systems acting in concerts. CPU Level Parallelism A computer’s CPU is commonly pictured as a device that operates on one instruction after another in a straight line, always completing one-step or instruction before a new one is started. But new CPU architectures have an inherent ability to do more than one thing at once. The logic of CPU chip divides the CPU into multiple execution units. Systems that have multiple execution units allow the CPU to attempt to process more than one instruction at a time. Two hardware features of modern CPUs support multiple execution units: the cache – a small memory inside the CPU. The pipeline is a small area of memory inside the CPU where instructions that
are next in line to be executed are stored. Both cache and pipeline allow impressive increases in CPU performances.
24
System level Parallelism It is the parallelism of multiple nodes coordinating to work on a problem in parallel that gives the cluster its power. There are other levels at which even more parallelism can be introduced into this system. For example if we decide that each node in our cluster will be a multi CPU system we will be introducing a fundamental degree of parallel processing at the node level. Having more than one network interface on each node introduces communication channels that may be used in parallel to communicate with other nodes in the cluster. Finally, if we use multiple disk drive controllers in each node we create parallel data paths that can be used to increase the performance of I/O subsystem. Software Parallelism Software parallelism is the ability to find well defined areas in a problem we want to solve that can be broken down into self-contained parts. These parts are the program elements that can be distributed and give us the speedup that we want to get out of a high performance computing system. Before we can run a program on a parallel cluster, we have to ensure that the problems we are trying to solve are amenable to being done in a parallel fashion. Almost any problem that is composed of smaller subproblems that can be quantified can be broken down into smaller problems and run on a node on a cluster. System-Level Middleware System-level middleware offers Single System Image (SSI) and high availability infrastructure for processes, memory, storage, I/O, and networking. The single system image illusion can be implemented using the hardware or software infrastructure. This unit focuses on SSI at the operating system or subsystems level.
25
A modular architecture for SSI allows the use of services provided by lower level layers to be used for the implementation of higher-level services. This unit discusses design issues, architecture, and representative systems for job/resource management, network RAM, software RAID, single I/O space, and virtual networking. A number of operating systems have proposed SSI solutions, including MOSIX, Unixware, and Solaris -MC. It is important to discuss one or more such systems as they help students to understand architecture and implementation issues. Message Passing Primitives Although new high-performance protocols are available for cluster computing, some instructors may want provide students with a brief introduction to message passing programs using the BSD Sockets interface Transmission Control Protocol/Internet Protocol (TCP/IP) before introducing more complicated parallel programming with distributed memory programming tools. If students have already had a course in data communications or computer networks then this unit should be skipped. Students should have access to a networked computer lab with the Sockets libraries enabled. Sockets usually come installed on Linux workstations. Parallel Programming Using MPI An introduction to distributed memory programming using a standard tool such as Message Passing Interface (MPI)[23] is basic to cluster computing. Current versions of MPI generally assume that programs will be written in C, C++, or Fortran. However, Java-based versions of MPI are becoming available.
26
Application-Level Middleware Application-level middleware is the layer of software between the operating system and applications. Middleware provides various services required by an application to function correctly. A course in cluster programming can include some coverage of middleware tools such as CORBA, Remote Procedure Call, Java Remote Method Invocation (RMI), or Jini. Sun Microsystems has produced a number of Java-based technologies that can become units in a cluster programming course, including the Java Development Kit (JDK) product family that consists of the essential tools and APIs for all developers writing in the Java programming language through to APIs such as for telephony (JTAPI), database connectivity (JDBC), 2D and 3D graphics, security as well as electronic commerce. These technologies enable Java to interoperate with many other devices, technologies, and software standards. Single System image A single system image is the illusion, created by software or hardware, that presents a collection of resources as one, more powerful resource. SSI makes the cluster appear like a single machine to the user, to applications, and to the network. A cluster without a SSI is not a cluster. Every SSI has a boundary. SSI support can exist at different levels within a system, one able to be build on another.
27
Single System Image Benefits
•
Provide a simple, straightforward view of all system resources and activities, from any node of the cluster
•
Free the end user from having to know where an application will run
•
Free the operator from having to know where a resource is located
•
Let the user work with familiar interface and commands and allows the administrators to manage the entire clusters as a single entity
•
Reduce the risk of operator errors, with the result that end users see improved reliability and higher availability of the system
•
Allowing centralize/decentralize system management and control to avoid the need of skilled administrators from system administration
•
Present multiple, cooperating components of an application to the administrator as a single application
28
•
Greatly simplify system management
•
Provide location- independent message communication
•
Help track the locations of all resource so that there is no longer any need for system operators to be concerned with their physical location
•
Provide transparent process migration and load balancing across nodes. • Improved system response time and performance
High speed networks
Network is the most critical part of a cluster. Its capabilities and performance directly influences the applicability of the whole system for HPC. Starting from Local/Wide Area Networks (LAN/WAN) like Fast Ethernet and ATM, to System Area Networks (SAN) like Myrinet and Memory Channel Eg. Fast Ethernet
• •
100 Mbps over UTP or fiber-optic cable MAC protocol: CSMA/CD
29
COMPONENTS OF CLUSTER COMPUTER
1. Multiple High Performance Computers a. PCs b. Workstations c. SMPs (CLUMPS) 2. State of the art Operating Systems a. Linux (Beowulf) b. Microsoft NT (Illinois HPVM) c. SUN Solaris (Berkeley NOW) d. HP UX (Illinois - PANDA) e. OS gluing layers(Berkeley Glunix) 3. High Performance Networks/Switches a. Ethernet (10Mbps), b. Fast Ethernet (100Mbps), c. Gigabit Ethernet (1Gbps) d. Myrinet (1.2Gbps) e. Digital Memory Channel f. FDDI 4. Network Interface Card a. Myrinet has NIC b. User-level access support 5. Fast Communication Protocols and Services a. Active Messages (Berkeley) b. Fast Messages (Illinois) c. U-net (Cornell) d. XTP (Virginia)
30
6. Cluster Middleware a. Single System Image (SSI) b. System Availability (SA) Infrastructure 7. Hardware a. DEC Memory Channel, DSM (Alewife, DASH), SMP Techniques 8. Operating System Kernel/Gluing Layers a. Solaris MC, Unixware, GLUnix 9. Applications and Subsystems a. Applications (system management and electronic forms) b. Runtime systems (software DSM, PFS etc.) c. Resource management and scheduling software (RMS) 10. Parallel Programming Environments and Tools a. Threads (PCs, SMPs, NOW..) b. MPI c. PVM d. Software DSMs (Shmem) e. Compilers f. RAD (rapid application development tools) g. Debuggers h. Performance Analysis Tools i. Visualization Tools 11. Applications a. Sequential b. Parallel / Distributed (Cluster-aware app.)
31
CLUSTER CLASSIFICATIONS
Clusters are classified in to several sections based on the facts such as 1)Application target 2) Node owner ship 3) Node Hardware 4) Node operating System 5) Node configuration. Clusters based on Application Target are again classified into two:
High Performance (HP) Clusters High Availability (HA) Clusters Clusters based on Node Ownership are again classified into two: • Dedicated clusters • Non-dedicated clusters Clusters based on Node Hardware are again classified into three:
• •
•
Clusters of PCs (CoPs)
•
Clusters of Workstations (COWs)
•
Clusters of SMPs (CLUMPs)
Clusters based on Node Operating System are again classified into:
•
Linux Clusters (e.g., Beowulf)
•
Solaris Clusters (e.g., Berkeley NOW)
•
Digital VMS Clusters
•
HP-UX clusters
32
Microsoft Wolfpack clusters Clusters based on Node Configuration are again classified into:
•
•
Homogeneous Clusters -All nodes will have similar architectures and run the same OSs
•
Heterogeneous Clusters- All nodes will have different architectures and run different OSs
ISSUES TO BE CONSIDERED
Cluster Networking If you are mixing hardware that has different networking technologies, there will be large differences in the speed with which data will be accessed and how individual nodes can communicate. If it is in your budget make sure that all of the machines you want to include in your cluster have similar networking capabilities, and if at all possible, have network adapters from the same manufacturer. Cluster Software You will have to build versions of clustering software for each kind of system you include in your cluster. Programming Our code will have to be written to support the lowest common denominator for data types supported by the least powerful node in our cluster. With mixed machines, the more powerful machines will have attributes that cannot be attained in the powerful machine. Timing This is the most problematic aspect of heterogeneous cluster. Since these machines have different performance profile our code will execute at
33
different rates on the different kinds of nodes. This can cause serious bottlenecks if a process on one node is waiting for results of a calculation on a slower node. The second kind of heterogeneous clusters is made from different machines in the same architectural family: e.g. a collection of Intel boxes where the machines are different generations or machines of same generation from different manufacturers. Network Selection There are a number of different kinds of network topologies, including buses, cubes of various degrees, and grids/meshes. These network topologies will be implemented by use of one or more network interface cards, or NICs, installed into the head-node and compute nodes of our cluster. Speed Selection No matter what topology you choose for your cluster, you will want to get fastest network that your budget allows. Fortunately, the availability of high speed computers has also forced the development of high speed networking systems. Examples are 10Mbit Ethernet, 100Mbit Ethernet, gigabit networking, channel bonding etc.
34
FUTURE TRENDS - GRID COMPUTING
As computer networks become cheaper and faster, a new computing paradigm, called the Grid has evolved. The Grid is a large system of computing resources that performs tasks and provides to users a single point of access, commonly based on the World Wide Web interface, to these distributed resources. Users consider the Grid as a single computational resource. Resource management software, frequently referenced as
middleware,
accepts jobs submitted by users and schedules them for
execution on appropriate systems in the Grid, based upon resource management policies. Users can submit thousands of jobs at a time without being concerned about where they run. The Grid may scale from single systems to supercomputer-class compute farms that utilize thousands of processors. Depending on the type of applications, the interconnection between the Grid parts can be performed using dedicated high-speed networks or the Internet. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, the Grid promises to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Several
35
examples of new applications that benefit from using Grid technology constitute a coupling of advanced scientific instrumentation or desktop computers with remote supercomputers; collaborative design of complex systems via high-bandwidth access to shared resources; ultra-large virtual supercomputers constructed to solve problems too large to fit on any single computer; rapid, large-scale parametric studies. The Grid technology is currently under intensive development. Major Grid projects include NASA’s Information Power Grid, two NSF Grid projects (NCSA Alliance’s Virtual Machine Room and NPACI), the European DataGrid Project and the ASCI Distributed Resource Management project. Also first Grid tools are already available for developers. The Globus Toolkit [20] represents one such example and includes a set of services and software libraries to support Grids and Grid applications.
36
CONCLUSION
Clusters are promising • Solve parallel processing paradox • Offer incremental growth and matches with funding pattern
•
New trends in hardware and software technologies are likely to make clusters more promising and fill SSI gap. • Clusters based supercomputers (Linux based clusters) can be seen everywhere!
37
21
There are some key concepts that must be understood when forming a cluster computing resource. Nodes or systems are the individual members of a cluster. They can be computers, servers, and other such hardware although each node generally has memory and processing capabilities. If one node becomes unavailable the other nodes can carry the demand load so that applications or services are always available. There must be at least two nodes to compose a cluster structure otherwise they are just called servers. The collection of software on each node that manages all cluster specific activity is called the cluster service. The cluster service manages all of the resources, the canonical items in the system, and sees then as identical opaque objects. Resources can be such things as
physical hardware devices, like disk drives and network cards, logical items, like logical disk volumes, TCP/IP addresses, applications, and databases.
22
When a resource is providing its service on a specific node it is said to be on-line. A collection of resources to be managed as a single unit is called a group. Groups contain all of the resources necessary to run a specific application, and if need be, to connect to the service provided by the application in the case of client systems. These groups allow administrators to combine resources into larger logical units so that they can be managed as a unit. This, of course, means that all operations performed on a group affect all resources contained within that group. Normally the development of a cluster computing system occurs in phases. The first phase involves establishing the underpinnings into the base operating system and building the foundation of the cluster components. These things should focus on providing enhanced availability to key applications using storage that is accessible to two nodes. The following stages occur as the demand increases and should allow for much larger clusters to be formed. These larger clusters should have a true distribution of applications, higher performance interconnects, widely distributed storage for easy accessibility and load balancing. Cluster computing will become even more prevalent in the future because of the growing needs and demands of businesses as well as the spread of the Internet.
Clustering Concepts
Clusters are in fact quite simple. They are a bunch of computers tied together with a network working on a large problem that has been broken down into smaller pieces. There are a number of different strategies we can use to tie them together. There are also a number of different software packages that can be used to make the software side of things work.
23
Parallelism The name of the game in high performance computing is parallelism. It is the quality that allows something to be done in parts that work independently rather than a task that has so many interlocking dependencies that it cannot be further broken down. Parallelism operates at two levels: hardware parallelism and software parallelism. Hardware Parallelism On one level hardware parallelism deals with the CPU of an individual system and how we can squeeze performance out of sub-components of the CPU that can speed up our code. At another level there is the parallelism that is gained by having multiple systems working on a computational problem in a distributed fashion. These systems are known as ‘fine grained’ for parallelism inside the CPU or having to do with the multiple CPUs in the same system, or ‘coarse grained’ for parallelism of a collection of separate systems acting in concerts. CPU Level Parallelism A computer’s CPU is commonly pictured as a device that operates on one instruction after another in a straight line, always completing one-step or instruction before a new one is started. But new CPU architectures have an inherent ability to do more than one thing at once. The logic of CPU chip divides the CPU into multiple execution units. Systems that have multiple execution units allow the CPU to attempt to process more than one instruction at a time. Two hardware features of modern CPUs support multiple execution units: the cache – a small memory inside the CPU. The pipeline is a small area of memory inside the CPU where instructions that
are next in line to be executed are stored. Both cache and pipeline allow impressive increases in CPU performances.
24
System level Parallelism It is the parallelism of multiple nodes coordinating to work on a problem in parallel that gives the cluster its power. There are other levels at which even more parallelism can be introduced into this system. For example if we decide that each node in our cluster will be a multi CPU system we will be introducing a fundamental degree of parallel processing at the node level. Having more than one network interface on each node introduces communication channels that may be used in parallel to communicate with other nodes in the cluster. Finally, if we use multiple disk drive controllers in each node we create parallel data paths that can be used to increase the performance of I/O subsystem. Software Parallelism Software parallelism is the ability to find well defined areas in a problem we want to solve that can be broken down into self-contained parts. These parts are the program elements that can be distributed and give us the speedup that we want to get out of a high performance computing system. Before we can run a program on a parallel cluster, we have to ensure that the problems we are trying to solve are amenable to being done in a parallel fashion. Almost any problem that is composed of smaller subproblems that can be quantified can be broken down into smaller problems and run on a node on a cluster. System-Level Middleware System-level middleware offers Single System Image (SSI) and high availability infrastructure for processes, memory, storage, I/O, and networking. The single system image illusion can be implemented using the hardware or software infrastructure. This unit focuses on SSI at the operating system or subsystems level.
25
A modular architecture for SSI allows the use of services provided by lower level layers to be used for the implementation of higher-level services. This unit discusses design issues, architecture, and representative systems for job/resource management, network RAM, software RAID, single I/O space, and virtual networking. A number of operating systems have proposed SSI solutions, including MOSIX, Unixware, and Solaris -MC. It is important to discuss one or more such systems as they help students to understand architecture and implementation issues. Message Passing Primitives Although new high-performance protocols are available for cluster computing, some instructors may want provide students with a brief introduction to message passing programs using the BSD Sockets interface Transmission Control Protocol/Internet Protocol (TCP/IP) before introducing more complicated parallel programming with distributed memory programming tools. If students have already had a course in data communications or computer networks then this unit should be skipped. Students should have access to a networked computer lab with the Sockets libraries enabled. Sockets usually come installed on Linux workstations. Parallel Programming Using MPI An introduction to distributed memory programming using a standard tool such as Message Passing Interface (MPI)[23] is basic to cluster computing. Current versions of MPI generally assume that programs will be written in C, C++, or Fortran. However, Java-based versions of MPI are becoming available.
26
Application-Level Middleware Application-level middleware is the layer of software between the operating system and applications. Middleware provides various services required by an application to function correctly. A course in cluster programming can include some coverage of middleware tools such as CORBA, Remote Procedure Call, Java Remote Method Invocation (RMI), or Jini. Sun Microsystems has produced a number of Java-based technologies that can become units in a cluster programming course, including the Java Development Kit (JDK) product family that consists of the essential tools and APIs for all developers writing in the Java programming language through to APIs such as for telephony (JTAPI), database connectivity (JDBC), 2D and 3D graphics, security as well as electronic commerce. These technologies enable Java to interoperate with many other devices, technologies, and software standards. Single System image A single system image is the illusion, created by software or hardware, that presents a collection of resources as one, more powerful resource. SSI makes the cluster appear like a single machine to the user, to applications, and to the network. A cluster without a SSI is not a cluster. Every SSI has a boundary. SSI support can exist at different levels within a system, one able to be build on another.
27
Single System Image Benefits
•
Provide a simple, straightforward view of all system resources and activities, from any node of the cluster
•
Free the end user from having to know where an application will run
•
Free the operator from having to know where a resource is located
•
Let the user work with familiar interface and commands and allows the administrators to manage the entire clusters as a single entity
•
Reduce the risk of operator errors, with the result that end users see improved reliability and higher availability of the system
•
Allowing centralize/decentralize system management and control to avoid the need of skilled administrators from system administration
•
Present multiple, cooperating components of an application to the administrator as a single application
28
•
Greatly simplify system management
•
Provide location- independent message communication
•
Help track the locations of all resource so that there is no longer any need for system operators to be concerned with their physical location
•
Provide transparent process migration and load balancing across nodes. • Improved system response time and performance
High speed networks
Network is the most critical part of a cluster. Its capabilities and performance directly influences the applicability of the whole system for HPC. Starting from Local/Wide Area Networks (LAN/WAN) like Fast Ethernet and ATM, to System Area Networks (SAN) like Myrinet and Memory Channel Eg. Fast Ethernet
• •
100 Mbps over UTP or fiber-optic cable MAC protocol: CSMA/CD
29
COMPONENTS OF CLUSTER COMPUTER
1. Multiple High Performance Computers a. PCs b. Workstations c. SMPs (CLUMPS) 2. State of the art Operating Systems a. Linux (Beowulf) b. Microsoft NT (Illinois HPVM) c. SUN Solaris (Berkeley NOW) d. HP UX (Illinois - PANDA) e. OS gluing layers(Berkeley Glunix) 3. High Performance Networks/Switches a. Ethernet (10Mbps), b. Fast Ethernet (100Mbps), c. Gigabit Ethernet (1Gbps) d. Myrinet (1.2Gbps) e. Digital Memory Channel f. FDDI 4. Network Interface Card a. Myrinet has NIC b. User-level access support 5. Fast Communication Protocols and Services a. Active Messages (Berkeley) b. Fast Messages (Illinois) c. U-net (Cornell) d. XTP (Virginia)
30
6. Cluster Middleware a. Single System Image (SSI) b. System Availability (SA) Infrastructure 7. Hardware a. DEC Memory Channel, DSM (Alewife, DASH), SMP Techniques 8. Operating System Kernel/Gluing Layers a. Solaris MC, Unixware, GLUnix 9. Applications and Subsystems a. Applications (system management and electronic forms) b. Runtime systems (software DSM, PFS etc.) c. Resource management and scheduling software (RMS) 10. Parallel Programming Environments and Tools a. Threads (PCs, SMPs, NOW..) b. MPI c. PVM d. Software DSMs (Shmem) e. Compilers f. RAD (rapid application development tools) g. Debuggers h. Performance Analysis Tools i. Visualization Tools 11. Applications a. Sequential b. Parallel / Distributed (Cluster-aware app.)
31
CLUSTER CLASSIFICATIONS
Clusters are classified in to several sections based on the facts such as 1)Application target 2) Node owner ship 3) Node Hardware 4) Node operating System 5) Node configuration. Clusters based on Application Target are again classified into two:
High Performance (HP) Clusters High Availability (HA) Clusters Clusters based on Node Ownership are again classified into two: • Dedicated clusters • Non-dedicated clusters Clusters based on Node Hardware are again classified into three:
• •
•
Clusters of PCs (CoPs)
•
Clusters of Workstations (COWs)
•
Clusters of SMPs (CLUMPs)
Clusters based on Node Operating System are again classified into:
•
Linux Clusters (e.g., Beowulf)
•
Solaris Clusters (e.g., Berkeley NOW)
•
Digital VMS Clusters
•
HP-UX clusters
32
Microsoft Wolfpack clusters Clusters based on Node Configuration are again classified into:
•
•
Homogeneous Clusters -All nodes will have similar architectures and run the same OSs
•
Heterogeneous Clusters- All nodes will have different architectures and run different OSs
ISSUES TO BE CONSIDERED
Cluster Networking If you are mixing hardware that has different networking technologies, there will be large differences in the speed with which data will be accessed and how individual nodes can communicate. If it is in your budget make sure that all of the machines you want to include in your cluster have similar networking capabilities, and if at all possible, have network adapters from the same manufacturer. Cluster Software You will have to build versions of clustering software for each kind of system you include in your cluster. Programming Our code will have to be written to support the lowest common denominator for data types supported by the least powerful node in our cluster. With mixed machines, the more powerful machines will have attributes that cannot be attained in the powerful machine. Timing This is the most problematic aspect of heterogeneous cluster. Since these machines have different performance profile our code will execute at
33
different rates on the different kinds of nodes. This can cause serious bottlenecks if a process on one node is waiting for results of a calculation on a slower node. The second kind of heterogeneous clusters is made from different machines in the same architectural family: e.g. a collection of Intel boxes where the machines are different generations or machines of same generation from different manufacturers. Network Selection There are a number of different kinds of network topologies, including buses, cubes of various degrees, and grids/meshes. These network topologies will be implemented by use of one or more network interface cards, or NICs, installed into the head-node and compute nodes of our cluster. Speed Selection No matter what topology you choose for your cluster, you will want to get fastest network that your budget allows. Fortunately, the availability of high speed computers has also forced the development of high speed networking systems. Examples are 10Mbit Ethernet, 100Mbit Ethernet, gigabit networking, channel bonding etc.
34
FUTURE TRENDS - GRID COMPUTING
As computer networks become cheaper and faster, a new computing paradigm, called the Grid has evolved. The Grid is a large system of computing resources that performs tasks and provides to users a single point of access, commonly based on the World Wide Web interface, to these distributed resources. Users consider the Grid as a single computational resource. Resource management software, frequently referenced as
middleware,
accepts jobs submitted by users and schedules them for
execution on appropriate systems in the Grid, based upon resource management policies. Users can submit thousands of jobs at a time without being concerned about where they run. The Grid may scale from single systems to supercomputer-class compute farms that utilize thousands of processors. Depending on the type of applications, the interconnection between the Grid parts can be performed using dedicated high-speed networks or the Internet. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, the Grid promises to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Several
35
examples of new applications that benefit from using Grid technology constitute a coupling of advanced scientific instrumentation or desktop computers with remote supercomputers; collaborative design of complex systems via high-bandwidth access to shared resources; ultra-large virtual supercomputers constructed to solve problems too large to fit on any single computer; rapid, large-scale parametric studies. The Grid technology is currently under intensive development. Major Grid projects include NASA’s Information Power Grid, two NSF Grid projects (NCSA Alliance’s Virtual Machine Room and NPACI), the European DataGrid Project and the ASCI Distributed Resource Management project. Also first Grid tools are already available for developers. The Globus Toolkit [20] represents one such example and includes a set of services and software libraries to support Grids and Grid applications.
36
CONCLUSION
Clusters are promising • Solve parallel processing paradox • Offer incremental growth and matches with funding pattern
•
New trends in hardware and software technologies are likely to make clusters more promising and fill SSI gap. • Clusters based supercomputers (Linux based clusters) can be seen everywhere!
37
