2013/12/12 meeting at Delta 621

1. WEEKLY REPORT
Thur., Dec 12, 2013
Pin Yi Tsai

2. OUTLINE
• So Far
• Compute Integral Image
Using Multiple Blocks
 640 x 480
 540 x 720
• Current Work
• Feature Value Extraction and Problems

3. RESULT
• 640*480
• Transpose at GPU ( Read from register; write to global memory)
• Using multiple blocks (columns / 8)
• Serial version: 5.11238 ms
• Parallel version: 1.85987 ms
• Speed up: 63% ( ( 5.11238- 1.85987 )/ 5.11238 )
======== Profiling result:
Time(%)
Time Calls
Avg
Min
Max Name
69.64 927.61us
2 463.81us 422.91us 504.70us computeByColumnTranspose(float*, float*, int, int)
15.69 209.02us
1 209.02us 209.02us 209.02us [CUDA memcpy HtoD]
14.67 195.36us
1 195.36us 195.36us 195.36us [CUDA memcpy DtoH]

4. RESULT (CONT.)
• 540x720
• Transpose at GPU ( Read from register; write to global memory)
• Using multiple blocks ( column / 8 )
• Serial version: 6.4817 ms
• Parallel version: 2.1217 ms
• Speed up: 64% ( ( 6.4817- 2.1217 ) / 6.7817 )
======== Profiling result:
Time(%)
Time Calls
Avg
Min
Max Name
67.69 1.06ms
2 531.80us 448.28us 615.32us
computeByColumnTranspose(float*, float*, int, int)
16.68 262.11us
1 262.11us 262.11us 262.11us [CUDA memcpy HtoD]
15.63 245.66us
1 245.66us 245.66us 245.66us [CUDA memcpy DtoH]

5. HOW TO EXTRACT FEATURE VALUE
1. Count how many features per image we can extract.
2. Use GPU to extract feature values with 11 types.
3. Output the data to a file.

6. HOW TO EXTRACT FEATURE VALUE (CONT.)
1. Count how many features per image we can extract.
Takes the same number of loops as computing feature values
 Find another way to assign the size of output data ?
2. Use GPU to extract feature values with 11 types.
Possibility of executing multiple types concurrently
 Use streams ? (still at the stage of checking the usage)
3. Output the data to a file.
Will the size be too huge?
( for a 16*16 image, 10640 feature values will be extracted )

7. The End