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Authors: Hank and Ken
Authors: Hank and Ken


== Concepts in Portable Performance and Modern Architectures ==
== Part 1: Concepts in Portable Performance and Modern Architectures ==


=== Chapter 1.1: Revolutions in Processor Architectures ===
=== Chapter 1.1: Revolutions in Processor Architectures ===
Line 40: Line 40:


Experts: James Reinders of Intel said he would participate
Experts: James Reinders of Intel said he would participate
=== Chapter 1.4: Programming Many-Core Architectures ===
Topics: CUDA, TBB, OpenMP, OpenCL, OpenACC
Dependencies: 1.1?  1.2 and 1.3?
=== Chapter 1.5: Functor Based Programming ===
Notes:
* function with no side effects can be applied in parallel to your heart’s content
* Kernels similar to functor based programming
Dependencies: None?
=== Chapter 1.6: Data-Parallel Primitives ===
Dependencies: None?
=== Chapter 1.7: Thrust ===
Dependencies: None?
=== Chapter 1.8: Higher-Levels Patterns on Top of Data-Parallel Primitives ===
* index-reversal, topology reconstruction, stream compaction
Dependencies: 1.6
=== Chapter 1.9: A Data Model for Representing Scientific Data ===
Expert: Jeremy Meredith, Dave Pugmire
Student: James Kress (at ORNL in Fall)
Dependencies: None?
== Part 2: The VTK-m Approach ==
=== Chapter 2.0: How to compile VTK-m. ===
* Download, CMake, dependencies -- Boost, etc)
Dependencies: None?
=== Chapter 2.1: Basic VTK-m Usage ===
Five important subsections:
# Memory Management (Array Handles)
# VTK-m’s Data Model
# Device Adapters (introduce topic: abstract model, supported devices, we’ll talk more about it later)
# Filters
# Example Programs
(This may take several students working)
Dependencies: None?
=== Chapter 2.2: Developing Algorithms in VTK-m ===
Three important subsections:
# Worklets and different worklet types, signatures
# Dispatchers
# Getting data from control to execution environment (harkens back to host-device)
Dependencies: 2.1?
=== Chapter 2.3: Device Adapters ===
*  how device adapters work
* list of things in a device adapter: (1) how to most between host and device and (2) collection of algorithms that are implemented.
* must do a scheduler/map, other DPP derived from it.  But often done over again for efficiency in a specific language
* how to write your own.  (this question may frame some of the points above)
Dependencies: 2.1 (or maybe 2.1 should distill a section from this?)
=== Chapter 2.4: Algorithms that Use Device Adapters Directly (no worklets) ===
=== Chapter 2.5: Worklets ===
* how they work
* how to create a new type
=== Chapter 2.6: Explore One Algorithm (Contour) In Depth ===
Question of how far to go.  Hank likes slides from CS, JM that should step-by-step
(more such chapters?)
=== Chapter 2.7: Here’s how VTK-m fits ===
* in situ, post-processing
* No MPI … you add it
* VisIt/ParaView
== Part 3: Advanced Topics ==
=== Chapter 3.1: search data structures ===
=== Chapter 3.2: the case for performance ===
talk about why we believe this approach will provide portable performance, and success stories (this would be XVis people, not UO students ... except for Matt Larsen)
=== Chapter 3.3: in practice ===
would people want to submit a few in practice success stories (this would be Xvis people, not UO students)

Revision as of 19:00, 6 August 2015

Book title: Achieving Portable Performance For Visualization, Using VTK-m

The book is organized into two parts:

  1. Concepts in Portable Performance and Modern Architectures
  2. The VTK-m Approach

Each chapter has dependencies listed. A dependency for chapter C1 on chapter C2 means that C2 should be complete (or in reasonable shape) before C1 is started, so that the terminology can be consistent.

Introduction Chapter

Chapter 0: Introduction

  • The Organization of this Book
  • The scope of the VTK-m: what it does, why it is useful

Dependencies: last chapter written (or at least all other chapters should be taking shape)

Authors: Hank and Ken

Part 1: Concepts in Portable Performance and Modern Architectures

Chapter 1.1: Revolutions in Processor Architectures

Topics:

  • threading vs vector / multi-core/many-core / host-device / memory hierarchies
  • historical discussion: Moore’s law, but Dinar scaling failed … how to deal with extra transistor
  • exascale power story, state of current Top500

Dependencies: none

Chapter 1.2: GPU

Dependencies: Chapter 1.1

Experts: Tom Fogal, NVIDIA?

Chapter 1.3: Xeon Phi

Dependencies: Chapter 1.1

Experts: James Reinders of Intel said he would participate

Chapter 1.4: Programming Many-Core Architectures

Topics: CUDA, TBB, OpenMP, OpenCL, OpenACC Dependencies: 1.1? 1.2 and 1.3?

Chapter 1.5: Functor Based Programming

Notes:

  • function with no side effects can be applied in parallel to your heart’s content
  • Kernels similar to functor based programming

Dependencies: None?

Chapter 1.6: Data-Parallel Primitives

Dependencies: None?

Chapter 1.7: Thrust

Dependencies: None?

Chapter 1.8: Higher-Levels Patterns on Top of Data-Parallel Primitives

  • index-reversal, topology reconstruction, stream compaction

Dependencies: 1.6

Chapter 1.9: A Data Model for Representing Scientific Data

Expert: Jeremy Meredith, Dave Pugmire Student: James Kress (at ORNL in Fall)

Dependencies: None?

Part 2: The VTK-m Approach

Chapter 2.0: How to compile VTK-m.

  • Download, CMake, dependencies -- Boost, etc)

Dependencies: None?

Chapter 2.1: Basic VTK-m Usage

Five important subsections:

  1. Memory Management (Array Handles)
  2. VTK-m’s Data Model
  3. Device Adapters (introduce topic: abstract model, supported devices, we’ll talk more about it later)
  4. Filters
  5. Example Programs

(This may take several students working)

Dependencies: None?

Chapter 2.2: Developing Algorithms in VTK-m

Three important subsections:

  1. Worklets and different worklet types, signatures
  2. Dispatchers
  3. Getting data from control to execution environment (harkens back to host-device)

Dependencies: 2.1?

Chapter 2.3: Device Adapters

  • how device adapters work
  • list of things in a device adapter: (1) how to most between host and device and (2) collection of algorithms that are implemented.
  • must do a scheduler/map, other DPP derived from it. But often done over again for efficiency in a specific language
  • how to write your own. (this question may frame some of the points above)

Dependencies: 2.1 (or maybe 2.1 should distill a section from this?)

Chapter 2.4: Algorithms that Use Device Adapters Directly (no worklets)

Chapter 2.5: Worklets

  • how they work
  • how to create a new type

Chapter 2.6: Explore One Algorithm (Contour) In Depth

Question of how far to go. Hank likes slides from CS, JM that should step-by-step (more such chapters?)

Chapter 2.7: Here’s how VTK-m fits

  • in situ, post-processing
  • No MPI … you add it
  • VisIt/ParaView

Part 3: Advanced Topics

Chapter 3.1: search data structures

Chapter 3.2: the case for performance

talk about why we believe this approach will provide portable performance, and success stories (this would be XVis people, not UO students ... except for Matt Larsen)

Chapter 3.3: in practice

would people want to submit a few in practice success stories (this would be Xvis people, not UO students)