1. MASS CUDA Performance Analysis and Improvement
Ahmed Musazay
Faculty Advisor: Dr. Munehiro Fukuda

2. MASS Multi Agent Spatial Simulation
Allows non-computing specialists to parallelize simulations
Concept of Place and Agent objects
Three versions: C++, Java, CUDA
High-level abstraction to non-computing specialists

3. CUDA C/C++ extension by NVidia
A heterogeneous parallel programming interface.
Host – CPU , and Device – GPU
Functions executing on the GPU are called Kernel functions
Take configuration parameters for number of threads
-fast, but difficult to use, hard to tune up perf
-utilize performance and also bring high level abstraction of mass

4. MASS-CUDA
Current version – written by Nathaniel Hart for Master’s thesis
Ported C++ version into current CUDA version
Object oriented- allows users to extend Place and Agent objects
Designed with intention of using multiple GPU cards
Nate’s work- porting from mass to cuda

5. Problem Performance issues Difficult to tune performance
Goal of project:
Understand MASS Library and how it works
Write unit tests to find where performance issues occur
Propose solutions that can be implemented to increase performance of MASS CUDA

6. Heat2D Fourier’s heat equation Place objects – Metal
Simulation describing spread of heat in a given region over period of time
Place objects – Metal
Ran at four different sizes
250x250, 500x500, 1000x1000, 2000x2000

7. Test Case: Running Heat2D - Primitive Array
Heat2D simulation using array of doubles
No objects created to contain information as opposed to MASS
Simulation functions written as kernel functions

8. Results

9. Proposed Solution
Store all data in MASS as user-defined primitive type arrays
Index mapping to unique element
Pros
Fast accesses
Can run larger simulations, requiring less heap memory overhead
Cons
User programmability

10. Test Case: Running Heat2D - Place objects
Ran simulation with same objects used in MASS, without using library function calls
Metal & MetalState derived from Library classes containing same memory and internal functions
Simulation functions re-written in CUDA as kernel functions

11. Results

12. Proposed Solution
Remove unnecessary functionality that may be slowing library down
Excessive memory transfers between host and device
Partitioning logic
Pros
Can work on adding only a single feature of library at time, making sure meeting performance standard
More computation spent on actual simulation rather than management
Cons
Scalability of library will be missing early in development

13. Test Case: Running Heat2D – Coalesced Accesses
Ran simulation using primitive values, but taking advantage of coalesced memory accesses
Kernel functions taking array parameters as native dimension – 2D array
cudaMallocPitch(), cudaMalloc3D()

14. Results

15. Proposed Solution
Let MASS run the simulation in its native dimension (1D, 2D, 3D)
Pros
Faster memory accesses, increasing performance
Cons
Extra overhead of determining dimensions to run function as
Will only be able to natively run up to 3 dimensions

16. Conclusion
Removing unused features, implementing one feature at a time
Coalesced memory accesses – using native array dimensions
Using primitive arrays
Consider : Shared memory

17. Final Words Relevant courses: Special thanks to:
CSS 430 Operating Systems
CSS 422 Hardware and Computer Organization
Special thanks to:
Dr. Fukuda
Nathaniel Hart