Embed Size (px)
Citation preview
CUDA Implementation of the Lattice Boltzmann MethodCSE 633 Parallel Algorithms
Andrew Leach
University at Buffalo
2 Dec 2010
A. Leach (University at Buffalo) CUDA LBM Nov 2010 1 / 16
Motivation
The Lattice Boltzmann Method(LBM) solves the Navier-Stokesequation accurately and efficiently.
Uniformity makes it easy to parallelize.
High volume of simple calculations make it ideal for GPGPUcomputing.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 2 / 16
Motivation
The Lattice Boltzmann Method(LBM) solves the Navier-Stokesequation accurately and efficiently.
Uniformity makes it easy to parallelize.
High volume of simple calculations make it ideal for GPGPUcomputing.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 2 / 16
Motivation
The Lattice Boltzmann Method(LBM) solves the Navier-Stokesequation accurately and efficiently.
Uniformity makes it easy to parallelize.
High volume of simple calculations make it ideal for GPGPUcomputing.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 2 / 16
LBM Degrees of Freedom
F5
F8
F2
F3
F7
F6
F1
F4
Each lattice point has an associated mass density
This mass density is projected in 9 directions
A. Leach (University at Buffalo) CUDA LBM Nov 2010 3 / 16
LBM Degrees of Freedom
F5
F8
F2
F3
F7
F6
F1
F4
Each lattice point has an associated mass density
This mass density is projected in 9 directions
A. Leach (University at Buffalo) CUDA LBM Nov 2010 3 / 16
LBM Stream
F8
F1
F5F2
F6
F3
F7F4
At each time step, each neighbor passes mass density
A. Leach (University at Buffalo) CUDA LBM Nov 2010 4 / 16
LBM Collision
F5
F8
F2
F3
F7
F6
F1
F4
Collision occurs with the accepted mass densities
Equillibrium condition is solved
New projected mass densities are assigned
A. Leach (University at Buffalo) CUDA LBM Nov 2010 5 / 16
LBM Collision
F5
F8
F2
F3
F7
F6
F1
F4
Collision occurs with the accepted mass densities
Equillibrium condition is solved
New projected mass densities are assigned
A. Leach (University at Buffalo) CUDA LBM Nov 2010 5 / 16
LBM Collision
F5
F8
F2
F3
F7
F6
F1
F4
Collision occurs with the accepted mass densities
Equillibrium condition is solved
New projected mass densities are assigned
A. Leach (University at Buffalo) CUDA LBM Nov 2010 5 / 16
LBM Boundary Conditions
Bounceback is implemented at solid boundaries
The inlet has predetermined mass density
The outlet accepts outward flow
A. Leach (University at Buffalo) CUDA LBM Nov 2010 6 / 16
LBM Boundary Conditions
Bounceback is implemented at solid boundaries
The inlet has predetermined mass density
The outlet accepts outward flow
A. Leach (University at Buffalo) CUDA LBM Nov 2010 6 / 16
LBM Boundary Conditions
Bounceback is implemented at solid boundaries
The inlet has predetermined mass density
The outlet accepts outward flow
A. Leach (University at Buffalo) CUDA LBM Nov 2010 6 / 16
Code: Data Structures
F1 HOST
Device
Data initialized as an array on host
Pitch stores the width of a row in memory, determined byCudaMallocPitch()
Memory is allocated on the device linear memory withCudaMallocArray()
Array copied from host to device with CudaMemcpy2D()
A. Leach (University at Buffalo) CUDA LBM Nov 2010 7 / 16
Code: Data Structures
F1 HOST
Device
Data initialized as an array on host
Pitch stores the width of a row in memory, determined byCudaMallocPitch()
Memory is allocated on the device linear memory withCudaMallocArray()
Array copied from host to device with CudaMemcpy2D()
A. Leach (University at Buffalo) CUDA LBM Nov 2010 7 / 16
Code: Data Structures
F1 HOST
Device
Data initialized as an array on host
Pitch stores the width of a row in memory, determined byCudaMallocPitch()
Memory is allocated on the device linear memory withCudaMallocArray()
Array copied from host to device with CudaMemcpy2D()
A. Leach (University at Buffalo) CUDA LBM Nov 2010 7 / 16
Code: Data Structures
F1 HOST
Device
Data initialized as an array on host
Pitch stores the width of a row in memory, determined byCudaMallocPitch()
Memory is allocated on the device linear memory withCudaMallocArray()
Array copied from host to device with CudaMemcpy2D()
A. Leach (University at Buffalo) CUDA LBM Nov 2010 7 / 16
Code: Textures
The stream step requires a lot of data retrieval
Texture memory has fast retrieval but limited space
Use cudaBindTextureToArray() to copy data as a texture
A. Leach (University at Buffalo) CUDA LBM Nov 2010 8 / 16
Code: Textures
The stream step requires a lot of data retrieval
Texture memory has fast retrieval but limited space
Use cudaBindTextureToArray() to copy data as a texture
A. Leach (University at Buffalo) CUDA LBM Nov 2010 8 / 16
Code: Textures
The stream step requires a lot of data retrieval
Texture memory has fast retrieval but limited space
Use cudaBindTextureToArray() to copy data as a texture
A. Leach (University at Buffalo) CUDA LBM Nov 2010 8 / 16
Code: Kernels
A kernel is launched on a grid of blocks
Each block consists of threads which will independently run thekernel(SIMD)
What follows is the Kernel for the stream() method. This exampleutilizes a lock-step texture look up.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 9 / 16
Code: Kernels
A kernel is launched on a grid of blocks
Each block consists of threads which will independently run thekernel(SIMD)
What follows is the Kernel for the stream() method. This exampleutilizes a lock-step texture look up.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 9 / 16
Code: Kernels
A kernel is launched on a grid of blocks
Each block consists of threads which will independently run thekernel(SIMD)
What follows is the Kernel for the stream() method. This exampleutilizes a lock-step texture look up.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 9 / 16
Code: Stream
A. Leach (University at Buffalo) CUDA LBM Nov 2010 10 / 16
Runtime Analysis
The following slides contain graphs comparing run times for the LBM on alaptop with 1.3 GHZ processor running sequential C code and a singleTesla GPU running parallel code in CUDA. The change in performancebased on block size is also explored.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 11 / 16
Sequential vs Parallel
A. Leach (University at Buffalo) CUDA LBM Nov 2010 12 / 16
Sequential vs Parallel
A. Leach (University at Buffalo) CUDA LBM Nov 2010 13 / 16
Sequential vs Parallel
A. Leach (University at Buffalo) CUDA LBM Nov 2010 14 / 16
Thank You
Thanks to Dr.Graham Pullan from Cambridge University for letting me useand modify his code.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 15 / 16
Bibliography
Alexander Wagner, A Practical Introduction to the Lattice BoltzmannMethod. North Dakota State University, March 2008.
Graham Pullan, A 2D Lattice Boltzmann Flow Solver Demo.http://www.many-core.group.cam.ac.uk/projects/LBdemo.shtml,University of Cambridge.
A. Leach (University at Buffalo) CUDA LBM Nov 2010 16 / 16
![From Lattice Boltzmann Method to Lattice Boltzmann Flux … · From Lattice Boltzmann Method to Lattice Boltzmann Flux Solver Yan Wang 1, ... flows [8,13–15], compressible flows](https://img.dokumen.tips/doc/110x75/5cadf91b88c9938f4d8c0cd6/from-lattice-boltzmann-method-to-lattice-boltzmann-flux-from-lattice-boltzmann.jpg)
![Improving computational efficiency of lattice Boltzmann ... · 1.1 The lattice Boltzmann method The lattice Boltzmann method [7] [20] is a relative new technique to CFD. Classical](https://img.dokumen.tips/doc/110x75/5f03952b7e708231d409c3df/improving-computational-efficiency-of-lattice-boltzmann-11-the-lattice-boltzmann.jpg)
