Presentation is loading. Please wait.

Presentation is loading. Please wait.

VTK-m Project Goals A single place for the visualization community to collaborate, contribute, and leverage massively threaded algorithms. Reduce the challenges.

Published byRoy Hawkins Modified over 9 years ago

Similar presentations

Presentation on theme: "VTK-m Project Goals A single place for the visualization community to collaborate, contribute, and leverage massively threaded algorithms. Reduce the challenges."— Presentation transcript:

1 VTK-m Project Goals A single place for the visualization community to collaborate, contribute, and leverage massively threaded algorithms. Reduce the challenges of writing highly concurrent algorithms by using data parallel algorithms

2 VTK-m Project Goals Make it easier for simulation codes to take advantage these parallel visualization and analysis tasks on a wide range of current and next-generation hardware.

3 VTK-m Architecture In-Situ Execution Data Parallel Algorithms Arrays Post Processing Worklets DataModel Filters Combines strengths of multiple projects: –EAVL, Oak Ridge National Laboratory –DAX, Sandia National Laboratory –PISTON, Los Alamos National Laboratory

4 VTK-m Arbitrary Composition VTK-m allows clients to access different memory layouts through the Array Handle and Dynamic Array Handle. – Allows for efficient in-situ integration – Allows for reduced data transfer Control EnvironmentExecution Environment Transfer Control EnvironmentExecution Environment

5 VTK-m Arbitrary Composition Point Arrangement Cells CoordinatesExplicitLogicalImplicit Structured Strided Separated Unstructured Strided Separated VTK-m Data Set VTK-m allows clients to construct data sets from cell and point arrangements that exactly match their original data – In effect, this allows for hybrid and novel mesh types

6 functor() Functor Mapping Applied to Topologies [Baker, et al. 2010]

7 functor() Functor Mapping Applied to Topologies [Baker, et al. 2010]

8 What We Have So Far Features –Core Types –Statically Typed Arrays –Dynamically Typed Arrays –Device Interface (Serial, Cuda, TBB) –Data Model –Field and Topology Worklet and Dispatcher

9 What We Have So Far Compiles with –gcc (4.8+), clang, msvc (2010+), icc, and pgi User Guide work in progress Ready for larger collaboration

10 2 x Intel Xeon CPU E5-2620 v3 @ 2.40GHz + NVIDIA Tesla K40c Data: 1024^3 (floats)

11 2 x Intel Xeon CPU E5-2620 v3 @ 2.40GHz + NVIDIA Tesla K40c Data: 1024^3 (floats)

12 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

13 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

14 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

15 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

16 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

17 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

18 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

19 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } }; Execution Environment Control Environment vtkm::cont::ArrayHandle inputHandle = vtkm::cont::make_ArrayHandle(input); vtkm::cont::ArrayHandle sineResult; vtkm::worklet::DispatcherMapField dispatcher; dispatcher.Invoke(inputHandle, sineResult);

20 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } }; vtkm::cont::ArrayHandle inputHandle = vtkm::cont::make_ArrayHandle(input); vtkm::cont::ArrayHandle sineResult; vtkm::worklet::DispatcherMapField dispatcher; dispatcher.Invoke(inputHandle, sineResult); Execution Environment Control Environment

21 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } }; vtkm::cont::ArrayHandle inputHandle = vtkm::cont::make_ArrayHandle(input); vtkm::cont::ArrayHandle sineResult; vtkm::worklet::DispatcherMapField dispatcher; dispatcher.Invoke(inputHandle, sineResult); Execution Environment Control Environment

22 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } }; vtkm::cont::ArrayHandle inputHandle = vtkm::cont::make_ArrayHandle(input); vtkm::cont::ArrayHandle sineResult; vtkm::worklet::DispatcherMapField dispatcher; dispatcher.Invoke(inputHandle, sineResult); Execution Environment Control Environment

23 struct Sine: public vtkm::worklet::WorkletMapField { typedef void ControlSignature(FieldIn<>, FieldOut<>); typedef _2 ExecutionSignature(_1); template VTKM_EXEC_EXPORT T operator()(T x) const { return vtkm::math::Sin(x); } };

24 Dispatcher Invoke Operations Convert polymorphic types to static types Check types Dispatcher-specific operations –Find domain length –Build index arrays Transport data from control to execution Run worklet invoke kernel Fetch thread-specific data Invoke worklet Push thread-specific data DispatcherMapField dispatcher; dispatcher.Invoke(inputHandle, sineResult);

25 struct ImagToPolar: public vtkm::worklet::WorkletMapField { typedef void ControlSignature( FieldIn, FieldOut, FieldOut ); typedef void ExecutionSignature(_1, _2, _3, _4); template<typename RealType, typename ImaginaryType, typename MagnitudeType, typename PhaseType> VTKM_EXEC_EXPORT void operator()(RealType real, ImaginaryType imag, MagnitudeType &magnitude, PhaseType &phase) const { magnitude = vtkm::math::Sqrt(real*real + imag*imag); phase = vtkm::math::ATan2(imaginary, real); } };

26 Coprocessing/In-situ Use of VTK and VTK-m –Process data in place using VTK-m –Visualize and analyze using VTK Bringing highly parallelized visualization and analytics in science to all Create bridges between VTK and VTK-m 26

Download ppt "VTK-m Project Goals A single place for the visualization community to collaborate, contribute, and leverage massively threaded algorithms. Reduce the challenges."

Similar presentations

GPU programming: CUDA Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials.

GPU programming: CUDA Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials.
GPU Virtualization Support in Cloud System Ching-Chi Lin Institute of Information Science, Academia Sinica Department of Computer Science and Information.

GPU Virtualization Support in Cloud System Ching-Chi Lin Institute of Information Science, Academia Sinica Department of Computer Science and Information.
ECE 562 Computer Architecture and Design Project: Improving Feature Extraction Using SIFT on GPU Rodrigo Savage, Wo-Tak Wu.

ECE 562 Computer Architecture and Design Project: Improving Feature Extraction Using SIFT on GPU Rodrigo Savage, Wo-Tak Wu.
Multi Agent Simulation and its optimization over parallel architecture using CUDA™ Abdur Rahman and Bilal Khan NEDUET(Department Of Computer and Information.

Multi Agent Simulation and its optimization over parallel architecture using CUDA™ Abdur Rahman and Bilal Khan NEDUET(Department Of Computer and Information.
DCABES 2009 China University Of Geosciences 1 The Parallel Models of Coronal Polarization Brightness Calculation Jiang Wenqian.

DCABES 2009 China University Of Geosciences 1 The Parallel Models of Coronal Polarization Brightness Calculation Jiang Wenqian.
Encapsulation by Subprograms and Type Definitions

Encapsulation by Subprograms and Type Definitions
CUDA Programming Lei Zhou, Yafeng Yin, Yanzhi Ren, Hong Man, Yingying Chen.

CUDA Programming Lei Zhou, Yafeng Yin, Yanzhi Ren, Hong Man, Yingying Chen.
Synergistic Execution of Stream Programs on Multicores with Accelerators Abhishek Udupa et. al. Indian Institute of Science.

Synergistic Execution of Stream Programs on Multicores with Accelerators Abhishek Udupa et. al. Indian Institute of Science.
Introduction What is GPU? It is a processor optimized for 2D/3D graphics, video, visual computing, and display. It is highly parallel, highly multithreaded.

Introduction What is GPU? It is a processor optimized for 2D/3D graphics, video, visual computing, and display. It is highly parallel, highly multithreaded.
Debunking the 100X GPU vs. CPU Myth: An Evaluation of Throughput Computing on CPU and GPU Presented by: Ahmad Lashgar ECE Department, University of Tehran.

Debunking the 100X GPU vs. CPU Myth: An Evaluation of Throughput Computing on CPU and GPU Presented by: Ahmad Lashgar ECE Department, University of Tehran.
Accelerating SQL Database Operations on a GPU with CUDA Peter Bakkum & Kevin Skadron The University of Virginia GPGPU-3 Presentation March 14, 2010.

Accelerating SQL Database Operations on a GPU with CUDA Peter Bakkum & Kevin Skadron The University of Virginia GPGPU-3 Presentation March 14, 2010.
Roadmap for Many-core Visualization Software in DOE Jeremy Meredith Oak Ridge National Laboratory.

Roadmap for Many-core Visualization Software in DOE Jeremy Meredith Oak Ridge National Laboratory.
Training Program on GPU Programming with CUDA 31 st July, 7 th Aug, 14 th Aug 2011 CUDA Teaching UoM.

Training Program on GPU Programming with CUDA 31 st July, 7 th Aug, 14 th Aug 2011 CUDA Teaching UoM.
Dax: Rethinking Visualization Frameworks for Extreme-Scale Computing DOECGF 2011 April 28, 2011 Kenneth Moreland Sandia National Laboratories SAND 2010-8034P.

Dax: Rethinking Visualization Frameworks for Extreme-Scale Computing DOECGF 2011 April 28, 2011 Kenneth Moreland Sandia National Laboratories SAND P.
9/13/20151 Threads ICS 240: Operating Systems –William Albritton Information and Computer Sciences Department at Leeward Community College –Original slides.

9/13/20151 Threads ICS 240: Operating Systems –William Albritton Information and Computer Sciences Department at Leeward Community College –Original slides.
CuMAPz: A Tool to Analyze Memory Access Patterns in CUDA

CuMAPz: A Tool to Analyze Memory Access Patterns in CUDA
Challenges Bit-vector approach Conclusion & Future Work A subsequence of a string of symbols is derived from the original string by deleting some elements.

Challenges Bit-vector approach Conclusion & Future Work A subsequence of a string of symbols is derived from the original string by deleting some elements.
1 Hardware Support for Collective Memory Transfers in Stencil Computations George Michelogiannakis, John Shalf Computer Architecture Laboratory Lawrence.

1 Hardware Support for Collective Memory Transfers in Stencil Computations George Michelogiannakis, John Shalf Computer Architecture Laboratory Lawrence.
Add Cool Visualizations Here Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary.

Add Cool Visualizations Here Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary.
Extracted directly from:

Extracted directly from:

Similar presentations

Ads by Google