Weekly Report-Reduction

Ph.D. Student: Leo Leedate: Oct. 30, 2009

Outline

7 Reduction implementations

Matrix multiplication

Protein Identification

Work plan

Five implementation discussed before

Interleaved Addressing With “%”

Interleaved Addressing Bank conflicts

Sequential addressing

Perform add when loading

Unroll the last warp

Implementation 6: Complete Unrolling

Specify block size as a function template parameter

Invoking template kernels

Could block size be a parameter?

Results

Implementation 7:

Results

Brief summary

Optimization Use efficient operator, not %; Avoid branch divergent in warps; Try to minimize the time of accessing the

global memory; Avoid bank conflict in shared memory; Unroll the loop as much as possible;

Matrix multiplication Time calculation

total time, in Main function double lf = clock(); for(int i=0; i<nMultipTimes; ++i) { RunTest(argc, argv, i, lfTotalTime, true); } total time = clock()-lf;

Compute time, in RunTest Function //create and start timer unsigned int nTimer = 0; cutilCheckError(cutCreateTimer(&nTimer)); cutilCheckError(cutStartTimer(nTimer));

matrixMul<<<grid, thread>>>(pDC, pDA, pDB, WA, WB);

//copy result from device to host cutilSafeCall(cudaMemcpy(pHC, pDC, nMemSizeC,

cudaMemcpyDeviceToHost)); //stop and destroy timer cutilCheckError(cutStopTimer(nTimer));

Experiments

WA, HA, WB

GPU / CPU

Comput time (ms) total time (ms)

16,16,16

GPU / CPU 45 / 15 24678 / 78

32,32,32

GPU / CPU 60 / 62 27250 / 203

48,80,128

GPU / CPU 225 / 861 26625 / 1203

128,256,512

GPU / CPU 4249 / 45829 35531 / 49328

512,512,512

GPU / CPU 27441 / 364232 70359 / 382062

2048,2048,2048

GPU 1697967 2020968

1697967->20, excluding the results transferring to the host

Mass Spectrometry Based Protein Identification

Mixed Proteins

>ipi|IPI00243451|IPI00243451.6 MDQHQHLNKTAESASSEKKKTRRCNGFKMFLAALSFSYIAKALGGIIMKISITQIERRFD…

TAESASSEKMFLAALSFSYIAK…

Digest

Mixed peptides

LC-MS/MS

Data

analyze

Protein sequence Peptide sequence

Merge

19-21-08 FT 893 MS2 9 avg #1 RT: 0.63 AV: 1 NL: 1.04E4T: FTMS + p NSI Full ms2 893.60@30.00 [ 500.00-1600.00]

600 700 800 900 1000 1100 1200 1300 1400m/z

0

10

20

30

40

50

60

70

80

90

100

Relat

ive A

bund

ance

928.6396

929.9735

720.3784823.9249

916.4733769.9116 955.7405

1008.5148

1097.6791676.8584

1229.5820 1358.6410900.2117663.0114588.3018 1115.5698 1412.59381348.38761239.3015

Tandem MS

Protein Identification

Computing intensive Usually identify 50,000 around Mass Spectrum;

Each MS will score with at least 10,000 peptides, and some time more than 100,000 peptides;

Some experiment costs more than a year, and parallel software is commonly used. Parallel identification related papers are published in JRP, RCM and Bioinformatics. But still no research are based on GPU.

Protein Identification

Mass spectrum Peptide19-21-08 FT 893 MS2 9 avg #1 RT: 0.63 AV: 1 NL: 1.04E4T: FTMS + p NSI Full ms2 893.60@30.00 [ 500.00-1600.00]

600 700 800 900 1000 1100 1200 1300 1400m/z

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e A

bund

ance

928.6396

929.9735

720.3784823.9249

916.4733769.9116 955.7405

1008.5148

1097.6791676.8584

1229.5820 1358.6410900.2117663.0114588.3018 1115.5698 1412.59381348.38761239.3015

Mass + tolerance

M1M2M3…

M200

TAESASSEK

Mass

P1P2P3…

P40

score

For each Mi in MS

check if it is in P

Got a vector MSB (0, 1, 0, 0, …)

For each Pi in P

check if it is in MS

Got a vector PB(1, 1, 0…)

Compute the cosine…

Keep the top-k score

Vector MS Vector P

400 EVDG

400 AAEE

400 PSTD

631 EMSVPS

699 TLKHLK

699 WDRDL

……

Peptide

>IQPSKANMETEPDQ…

>DEAVPPPALQLQFN…

>RQRAILKVMNTIGGE……

Protein

Protein Identification

Protein Identification

X! Tandem The most efficient open source software Parallel version Not too large!

Other works

Read parallel computing books;

Learn MLP, data mining.

Work plan

Data mining homework;

K-means debug and test.

Thanks