Grid applications
- 1. Prof. Neeraj Bhargava Pooja Dixit Department of Computer Science School of Engineering & System Sciences MDS, University Ajmer, Rajasthan, India 1
- 2. Distributed supercomputing High-throughput computing On-demand computing Data-intensive computing Collaborative computing 2
- 3. Aggregate computational resources to tackle problems that cannot be solved by a single system. Examples: climate modeling, computational chemistry Challenges include: – Scheduling scarce and expensive resources – Scalability of protocols and algorithms – Maintaining high levels of performance across heterogeneous systems 3
- 4. Schedule large numbers of independent tasks. Goal: exploit unused CPU cycles (e.g., from idle workstations) Unlike distributed computing, tasks loosely coupled Examples: parameter studies, cryptographic problems On-demand computing: • Use Grid capabilities to meet short-term requirements for resources that cannot conveniently be located locally. • Unlike distributed computing, driven by cost-performance concerns rather than absolute performance. • Dispatch expensive or specialized computations to remote servers. Data-intensive computing: • Synthesize data in geographically distributed repositories • Synthesis may be computationally and communication intensive. • Examples: – High energy physics generate terabytes of distributed data, need complex queries to detect ―interesting‖ events. – Distributed analysis of Sloan Digital Sky Survey data 4
- 5. Collaborative computing: Enable shared use of data archives and simulations Examples: – Collaborative exploration of large geophysical data sets Challenges: – Real-time demands of interactive applications – Rich variety of interactions 5
- 6. Advantages: Exploit Underutilized resources. CPU Scavenging, Hotspot leveling. Resource Balancing. Virtualized resources across an enterprise. (Like Data Grids, Compute Grids.). Enable collaboration for virtual organizations. Flexible, Secure, Coordinated resource sharing. Give worldwide access to a network of distributed resources. 6
- 7. Disadvantages: Need for interoperability when different groups want to share resources. –Diverse components, policies, mechanisms –E.g., standard notions of identity, means of communication, resource descriptions Need for shared infrastructure services to avoid repeated development, installation. –E.g., one port/service/protocol for remote access to computing, not one per tool/application –E.g., Certificate Authorities: expensive to run But how do I develop robust, secure, long-lived, well- performing applications for dynamic, heterogeneous Grids? I need, presumably: –Abstractions and models to add to speed/robustness/etc. of development. –Tools to ease application development and diagnose common problems. –Code/tool sharing to allow reuse of code components developed by others. 7