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. Now it runs everywhere. It has all the abstractions dealing with various aspects of hardware, architecture. Multiple layers abstraction because it was useful.