Weekly ReportStart learning GPU

Ph.D. Student: Leo Leedate: Sep. 18, 2009

Outline

• References

• Courses study

• Development

• Work plan

Outline

• References

• Courses study

• Development

• Work plan

References

• K-Means on commodity GPUs with CUDA– http://portal.acm.org/citation.cfm?id=1579193.1579654&coll=Por

tal&dl=GUIDE&CFID=52122012&CFTOKEN=42909759

• Accelerating K-Means on the Graphics Processor via CUDA– http://portal.acm.org/citation.cfm?id=1547557.1548166&coll=Por

tal&dl=GUIDE&CFID=53240258&CFTOKEN=63251930

• Fast Support Vector Machine Training and Classification on Graphics Processors– http://portal.acm.org/citation.cfm?id=1390156.1390170&coll=Por

tal&dl=GUIDE&CFID=53246314&CFTOKEN=25986930

K-Means on commodity GPUs with CUDA

• Introduction: – OpenMP has too much message communication overhead.– GPU is becoming common.– Compared with Shuai Che, puts new centroids recalculation step

also onto GPU and algorithm performance thus becomes better.

• GPGPU– The challenge in mapping a computing problem efficiently on a

GPU through CUDA is to store frequently used data items in the fastest memory, while keeping as much of the data on the device as possible.

– digital investigation, physics simulation, molecular dynamics.

K-Means on commodity GPUs with CUDA

• K-Means algorithm on GPU– Data objects assignment, two strategies

• Centroids-oriented-when the number of processors is small;• Data objects-oriented, adopted in this paper.

– K centroids recalculation• Massive condition statements are not suitable to the stream

processor model of GPUs• Host rearranges all data objects and counts the number of

data objects contained by each cluster.

– GPU based K means

K-Means on commodity GPUs with CUDA

• Performance analysis

K-Means on commodity GPUs with CUDA

K-Means on commodity GPUs with CUDA

• Pros and cons– Describe a GPU based k-Means algorithm

and achieve a speed up of 10;

– Does not have enough comparison, especially with other GPU base algorithms.

Fast SVM Training and Classification on GPU

• Introduction– SVM could be adapted to parallel computers.

– SVM is widely used.

– Training and classification are computationally intensive.

Fast SVM Training and Classification on GPU

• C-SVM– SVM Training

– SMO

Fast SVM Training and Classification on GPU

• SVM Classification

Fast SVM Training and Classification on GPU

• Graphics Processors– General purpose;

– More aggressive memory subsystems;

– Peak performance is usually impossible to achieve, but GPU still has significant speedups;

– True round to nearest even rounding on IEEE single precision datatypes and promise double precision in the near future.

– Nvidia GeForce 8800 GTX

– CUDA

Fast SVM Training and Classification on GPU

• SVM Training Implementation– Map reduce: computing f is the map, finding b

and I is the reduction.

Fast SVM Training and Classification on GPU

• Results, compared with LibSVM

Fast SVM Training and Classification on GPU

• Results, compared with LibSVM

Fast SVM Training and Classification on GPU

Summary

• GPU related paper outline– ** algorithm is useful and computational intensive;– GPU and CUDA is powerful;– Implement the algorithm on GPU;– Results, compared with CPU-based algorithm and

others’ GPU-based algorithm.

• New algorithms or better speedup.– K-means is hot;– K-nn, SVM, Apriori appeared.– What is ours focus?

Outline

• References

• Courses study– Data mining, Security, CUDA Programming

• Development

• Work plan

CUDA Programming

• On-line class– Introduction– Basic– Memory– Threads– Application performance– Floating-point

Outline

• References

• Courses study

• Development– Matrix multiply, read k-means and k-nn.

• Work plan

Outline

• References

• Courses study

• Development

• Work plan

Work plan

• Continue read the papers.

• Read the code of k-means and k-nn in details.

• Data mining– SVM and C4.5

• Thanks for you listening