- How effectively are Monte-Carlo methodology and Grid-Computing technology used in Finance for Corporate Performance Measurement?
- How extensively are these used in different business sectors/industries for Corporate Performance purposes?
- In terms of costs and benefits, how can the trade-off between short-term and long-term of operating Monte-Carlo and Grid-Computing be evaluated?
- Grid Computing and Cloud Computing are not the same, although they are mostly used synonymously in all-day life. Grind Computing has the ambitious vision to "connect and share" heterogeneous hardware for specific high-performance computing (e.g. running complex and resource-intensive calculations with distributed CPUs, RAMs, etc.), while Cloud Computing is till now mostly focused on services used as an interconnection of storage space and telecommunication: http://en.wikipedia.org/wiki/Grid_computing.
- There are several scientific Grid Computing platforms, like e.g. BOINC; but for the Finance world this seems to be quite in an initial phase ...
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Data Notes on Finance and Grid Computing
Is there any relationship between MC and Grid? I'm not in finance, but MC seems like a calculation technique, and Grid seems like the old-fashioned term for Cloud - just a way to manage resources (MC or not).
MC has a reputation for being very compute intensive, but compact. That is, lots of flops, but very few bytes (input or output). This is the canonical niche for GP-GPU, where the main challenge is lots of operations without going off-chip. There's no reason this can't be done in a Grid or Cloud, but also no real affinity.
Grid or Cloud is mostly about dynamic range: if you need 20k cores for 10 hours, you'd much rather get them from an existing pooled resource, rather than buying and running 1k machines. Burstiness and low duty cycle are why renting makes sense - if your usage is relatively constant (perhaps after pooling among your peers), and high-duty-cycle, then it makes little sense to pay the huge prices from a Cloud provider like Amazon EC2. It is a common misconception that large-scale providers have vast economies of scale. Once you get past a fairly modest compute facility (say a couple 15KW racks of compute nodes), all costs are basically linear.
Grid computing system is a widely distributed resource for a common goal. It is Brother of Cloud Computing and Sister of Supercomputer. We can think the grid is a distributed system connected to a single network. This types of computing work with the large volume of files. Basically, it is a cluster types system. So people call it cluster computing.
Source: https://www.cloudwebhostingtips.com/grid-computing-vs-cloud-computing-supercomputer/
Grid computer tends to be more geographically disperse and heterogeneous by nature. Grid network also has various types. A single grid is like dedicated connection but a common grid perform multiple tasks.
The size of the grid is large. So grid computing is like supercomputing. It consists of many network, computer, and middleware. Grid computer is dedicated to some specific function of the large volume of data. In the grid process, each task divided into a various process. All the process starts execution simultaneously on a different computer. As a result, very few seconds needs to execute and enjoy the flavor of supercomputing.
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