Exploiting Computing Power of GPU for Data Mining Application

1. Exploiting Computing Power of GPU for Data Mining Application Wenjing Ma, Leonid Glimcher, Gagan Agrawal

2. Outline of contents • Background of GPU computing • Parallel data mining • Challenges of data mining on GPU • GPU implementation • k-means • EM • kNN • Apriori • Experiment results • Results of kmeans and EM • Features of applications that are suitable for GPU computing • Related and future work

3. Background of GPU computing • Multi-core architectures are becoming more popular in high performance computing • GPU is inexpensive and fast • CUDA is a high level language that supports programming on GPU

4. CUDA functions • Host function • Called by host and executed on host • Global function • Called by host and executed on device • Device function • Called by device and executed on device

5. Architecture of GeForce 8800 GPU (1 multiprocessor)

6. Parallel data mining • Common structure of data mining applications (adopted from Freeride) • { * Outer Sequential Loop * } • While () { • { * Reduction Loop * } • Foreach (element e) { • (i,val) = process(e); • Reduc(i) = Reduc(i) op val; • } • }

7. Challenges of data mining on GPU • SIMD shared memory programming • 3 steps involved in the main loop • Data read • Computing update • Writing update

8. Computing update copy common variables from device memory to shared memory nBlocks = blockSize/ thread number For i=1 to nBlocks { each thread process 1 data element } Global reduction

9. GPU Implementation • k-means • Data are points (say, 3 dimension) • Start with k clusters • Find the nearest cluster for each point • determine the k centroids from the points assigned to the corresponding center • Repeat until the assignments of points don’t change

10. GPU version of kmeans Device function: Shared_memory center nBlocks = blockSize / thread_number tid = thread_ID For i = 1 to nBlocks min = 0; For j = 1 to k dis = distance(data[tid], center[j]) if (dis < min) min = dis min index = i update[tid][min index] (data[tid],dis) Thread 0 combines all copies of update

11. Other applications • EM • E step and M step, different amount of computation • Apriori • Tree-structured reduction objects • Large amount of updates • kNN

12. Experiment results • k-means and EM has the best performance when using 512 threads/block and 16 or 32 thread blocks • kNN and apriori hardly get good speedup with GPU

13. k-means(10MB points)

14. k-means (continued)(20MB points)

15. EM (continued)(512K points)

16. EM (continued)(1M points)

17. Features of applications that are suitable for GPU computing • the time spent on processing the data must dominate the I/O cost • the size of the reduction object needs to be small enough to have a replica for each thread in device memory • using the shared memory to store frequently accessed data

18. the time spent on processing the data must dominate the I/O cost I/O computing

19. the size of the reduction object needs to be small enough to have a replica for each thread in device memory • No locking mechanism on GPU • The access to the reductionobjects are unpredictable

20. using the shared memory to store frequently accessed data • Accessing device memory is very time consuming • Shared memory serves as a high speed cache • For non-read-only data elements on shared memory, we also need replica for each thread

21. Related work • Freeride • Other GPU computing languages • The usage of GPU computation in scientific computing

22. Future work • Middleware for data mining on GPU • Provide some compilation mechanism for data mining applications on MATLAB • Enable tuning of parameters that can optimize GPU computing

23. Thank you! Questions?