DistOS 2014W Lecture 24

The Landscape of Parallel Computing Research: A View from Berkeley

• What sort of applications can you expect to run on distributed OS/parallize?
• How do you scale up
• We can't rely on processor improvements to provide speed-ups
• The proposed computational models that need more processor power don't really apply to regular
• Users would see the advances with games primarily
• More reliance in cloud computing in recent years

7 Dwarfs

• Dense Linear Algebra
  • Hard to parallize
• Sparse Linear Algebra
• Spectral Methods
• N-Body Methods
• Structured Grids
• Unstructured Grids
• Monte Carlo

Extended Dwarfs

• Combinational Logic
• Graph Traversal
• Dynamic Programming
• Backtrack/Branch + Bound
• Construct Graphical Models
• Finite State Machines

Features

• Pretty impressive on getting everyone to sign off on the report
• Connection to MapReduce
• Programs that run on distributed operating systems - applications that can be expected to be massively parallel - what sort of computational model is needed - Abstractions needed on top of the stack.
• Predictions about the processing power
• GPU's do have 1000 or more cores
• Desktop cores have not gotten that fast over the past years. They just don't run fast enough.
• Games are the only things that can't be run over the time on single thread
• Low power
• Being able to run a smart phone with 100's of transistors - stalled with the sequential processing
• Why do we need the additional processing power for ? - Games - Games - Games
• Doomsday of the IT industry
• Massive change in mobile and cloud over the past five years
• Linux a very general operating system when it started at first - hard coded to 8086 processor specialized to run on the box