Term paper: ibm supercomputer, watson

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Reference no: EM13812 , Length: 8 Pages

Assignment 2  (Computer architecture) : Term Paper: IBM Supercomputer, Watson (Note 8 page)

In February 2011, Watson, the IBM Supercomputer defeated Jeopardy's titans of trivia Ken Jennings and Brad Rutter on TV in front of perhaps millions of Jeopardy fans. Watson is a sophisticated computer system that uses natural language processing and artificial intelligence to answer questions. Watson is able to access 200 million pages of structured and unstructured content stored in four terabytes of disk storage. Watson is made up of a cluster of ninety IBM Power 750 servers with a total of 2880 POWER7 processors and 16 TB of RAM.

Watson is now being applied to help with medical diagnoses for WellPoint (https://prn.to/nqKrel), help analyze customer needs and process financial, economic and client data for Citigroup (https://buswk.co/zUUUOF), and other areas.  

Write an eight to ten (8) page paper in which you:
Study and closely examine the following Watson hardware components:

1.     Processors - Watson uses close to 3,000 processors to execute instructions and process data.

a.     Explain how using 3,000 processors benefits Watson's computing capabilities.

b.    Determine whether or not doubling, tripling, or quadrupling the number of processors would have an effect. Explain why or why not.

2.     RAM - Watson uses 16 terabytes of Random Access Memory.

a.     Determine whether large amounts of RAM help Watson process data pages faster.

b.    Predict the possible effects of doubling the amount of Watson's RAM.

3.     Networking - Watson uses a cluster of servers that requires a communication network. Explain the requirements for the communication network between the servers in terms of throughput, transmission speed, and protocols for reliability.

4.     Disk storage - Watson analyzes a large number of pages per unit time to extract analytic information. Explain how Watson is able to store large amounts of data. Include the disk space requirements, the access times for reading and writing, throughput, and disk architecture.

Analyze the software components used to build Watson. In your analysis address the following components:

5.     IBM DeepQA - DeepQA is a massively parallel probabilistic evidence-based architecture that allows Watson to analyze, synthesize evidence, rank, and produce answers. Determine at least five challenges of integrating many components used in the DeepQA architecture (see the DeepQA architecture diagram at https://bit.ly/HGtesV).

6.     SUSE Linux Enterprise Server 11 O / S - IBM decided to use this operating system instead of a Windows based or Apple OS X Lion.

  • Explain the reasons for using SUSE Linux as the operating system for Watson.
  • Determine if you would use a different operating system for Watson and explain why or why not.

7.     Apache Hadoop framework - This framework allows for building applications for distributed processing. Determine the challenges of data accessibility to a clustered environment in terms of:

a.     Reliability

b.    Scalability

c.     Data errors and failures

d.    High-availability

8.     Apache UIMA framework - This is the Unstructured Information Management framework that enables applications to be decomposed into components. Explain the reasons for the Java-based UIMA framework in Watson, particularly when it comes to data flow between Watson components.

9.     Watson has been applied to other environments such as the health care and communications ecosystems.

  • Identify the components that allow Watson to be usable in these systems (i.e. exchangeability of components).
  • Determine if Watson can be applied to any IT ecosystem. Explain why or why not.

10.  Watson applies natural language processing to solve problems and answer questions. Determine whether computer systems that support this type of processing can be considered to be equal or equivalent to humans' ability to think. 

11.  Use at least three (3) quality resources in this assignment. 

Note: Wikipedia and similar Websites do not qualify as quality resources.

Verified Expert

Reference no: EM13812

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