VTK/Multicore Vision: Difference between revisions

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(New page: == 1. Accelerate algorithms that need access to the whole dataset == * Examples: ** Streamlines ** Graph algorithms * What is needed: ** Multi***threading abstraction *** vtkMultiThreader ...)
 
 
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== 1. Accelerate algorithms that need access to the whole dataset ==
The goal of this document is to provide an overview of the vision that the working team (SCI, Sandia, Kitware) has for the shared memory parallelism improvements to VTK.
* Examples:
 
** Streamlines
As a first step, we will work on collecting use cases that we want to address. This will help us to scope what we will focus on.
** Graph algorithms
 
* What is needed:
Some high level use-cases are listed below.
** Multi***threading abstraction
 
*** vtkMultiThreader
== 1. Direct acceleration of algorithms ==
*** What about OpenMP, Intel TBB?
 
** Thread safe data structures
This is the classical approach VTK takes to take advantage of shared-memory parallel architectures. A few of the scientific vis. algorithms and many of the imaging algorithms can run multi-threaded. The input data structures are usually not pre-partitioned but shared by all threads. For algorithms that cannot stream, this is the only way to make use of multiple cores. Examples:
* Streamlines
* Some graph algorithms
 
We currently use vtkMultiThreader to manage multiple threads. Are we interested in investigating OpenMP, Intel TBB, GPU parallelism?
 
* Pros:
* Pros:
** Scalable
** Does not require streaming
* Cons:
* Cons:
** Filters need to change
** Filters need to change
** Needs thread safe data structures
== 2. Pipeline parallelism ==
== 2. Pipeline parallelism ==
* Analyze the pipeline
 
* Assign different filters to different threads
We informally refer to pipeline parallelism as the execution model where the executive is responsible of distributing tasks to filters that are (possibly) running on multiple threads. This requires that the executive analyzes the pipeline to figure out task distribution and that it assigns tasks in a way that makes use of multiple cores. This sort of parallelism is closely tied to streaming. Without streaming, only branching pipelines can be parallelized and almost no scalability is reached.
 
* Pros:
* Pros:
** Automatic performance improvement for all filters
** Automatic performance improvement for all filters
* Cons:
* Cons:
** It is only useful for:
** It is only useful when streaming
*** Streaming
 
*** Branching pipelines
=== Streaming ===
== 3. Streaming ==
* All types of data:
* All types of data:
** Structured data
** Structured data
Line 28: Line 35:
** ...
** ...
* How do we break the data?
* How do we break the data?
== 4. How do we bring (1), (2) and (3) together? ==
 
* Do algorithms need to provide more meta***data?
== 4. How do we bring (1), (2) and streaming together? ==
* Do algorithms need to provide more meta-data?
** I can work with pieces
** I can work with pieces
** I need the whole thing
** I need the whole thing
** I scale up to N processors?
** I scale up to N processors?
== 5. What about interactive applications ==
* Using separate threads to run the pipeline and to control user interface/rendering

Latest revision as of 15:23, 25 August 2009

The goal of this document is to provide an overview of the vision that the working team (SCI, Sandia, Kitware) has for the shared memory parallelism improvements to VTK.

As a first step, we will work on collecting use cases that we want to address. This will help us to scope what we will focus on.

Some high level use-cases are listed below.

1. Direct acceleration of algorithms

This is the classical approach VTK takes to take advantage of shared-memory parallel architectures. A few of the scientific vis. algorithms and many of the imaging algorithms can run multi-threaded. The input data structures are usually not pre-partitioned but shared by all threads. For algorithms that cannot stream, this is the only way to make use of multiple cores. Examples:

  • Streamlines
  • Some graph algorithms

We currently use vtkMultiThreader to manage multiple threads. Are we interested in investigating OpenMP, Intel TBB, GPU parallelism?

  • Pros:
    • Does not require streaming
  • Cons:
    • Filters need to change
    • Needs thread safe data structures

2. Pipeline parallelism

We informally refer to pipeline parallelism as the execution model where the executive is responsible of distributing tasks to filters that are (possibly) running on multiple threads. This requires that the executive analyzes the pipeline to figure out task distribution and that it assigns tasks in a way that makes use of multiple cores. This sort of parallelism is closely tied to streaming. Without streaming, only branching pipelines can be parallelized and almost no scalability is reached.

  • Pros:
    • Automatic performance improvement for all filters
  • Cons:
    • It is only useful when streaming

Streaming

  • All types of data:
    • Structured data
    • Unstructured data
    • Text (documents)
    • ...
  • How do we break the data?

4. How do we bring (1), (2) and streaming together?

  • Do algorithms need to provide more meta-data?
    • I can work with pieces
    • I need the whole thing
    • I scale up to N processors?