New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

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Figure 6.2.3: Cell performance values for eight SPEs. Note that in a PS3 only six SPEs are available for calculations. These benchmarks nicely reflect the optimization of the Cell towards single precision operations. 158CHAPTER 6. PROTEOMICS.NET - PRODUCT-ORIENTED CASE STUDIES 6.2.4 Embedding of Specialized Hardware on the Example of a Playstation 3 Background In the last years specialized multi-core hardware such as IBM’s Cell/BE processor or Graphic Processing Units (GPUs) from AMD and NVIDIA were released to the consumer market. Although having a radically different hardware architecture opposed to the common desktop PC, they are really interesting for computational tasks because they are equipped with many (co-) processing units for calculations. A smaller version (seven opposed to 16 cores) of IBM’s Cell/BE processor is built into Sony’s Playstation 3 (PS3) and can be purchased for about 400 EUR (in December 2007). At 3.2 GHz, each SPE gives a theoretical 25.6 GFLOPS of single precision performance which add up to about 190 GFLOPS. Since in a PS3 only six SPEs are available for calculations (one is deactivated and another one reserved for the OS) about 150 GFLOPs can be achieved. This is about 15 times faster than a common Intel desktop PC (single core, running at 3.6GHz). In a typical usage scenario, Linux is installed on the Playstation using a kernel extension by IBM. Using IBM’s Cell SDK programs can be written in the C++ programming language that support the Cell’s instruction set. This system will load the SPEs with these programs (similar to threads) which can then perform the given task in parallel. Since the playstation has only a limited memory (RAM) of 512MB and reaches the good performance only in singleprecision mode not all algorithms will run well on this technology. Further, each SPE has only 256KB of local memory that can be used for computations. Porting Peak Picking Motivated by these performance figures we ported the core of our reference worker from Java to C. This “Playstation BaseWorker” can communicate with the proteomics.net platform, request jobs and send back results. Given the above mentioned limitations, we choose to implement and optimize the peak picking algorithm (see section 3.4.3) because the data can be processed linearly and easily splitted up into small packages.
6.2. CASE STUDIES 159 The peak picking algorithm changed slightly opposed to the original version on a single core CPU and is now as follows: 1. Load a 1D spectrum into main memory (about 670KB in 54.000 x/y data points) 2. Split spectrum into pieces of size min(256KB, spectrum size[KB] #SP Us ) 3. Until pieces are available: Send pieces to SPEs to find peaks 4. Wait for all SPEs to finish 5. Merge results Results The results are shown in Table 6.2.4. Machine Test # SPEs Time [ms] per spectrum Intel # w/o fitting 1 8200 Intel # w/ fitting 1 13300 PS3 w/o fitting 6 115 PS3 w/ fitting 1 2250 PS3 w/ fitting 6 750 QS20 w/ fitting 1 2100 QS20 w/ fitting 6 730 QS20 w/ fitting 12 410 QS20 w/ fitting 16 350 Table 6.2.1: This shows the PS3 performance tests. # : Standard desktop single-core PC at 3.2GHz 6.2.5 Integrating of Non-Grid Applications on the Example of Matlab Scripts and Linux Binaries Background As explained in the previous chapter, writing QAD Grid applications is not a very complex task but still, some modifications must be made to standard code. Thanks to the QAD Grid worker concept, there are ways to circumvent even this requirement as we will show below for algorithms existing as Matlab script and for binaries where the source code obviously cannot be modified. Besides the obvious advantages distributed computation offers, e.g. Load balancing, another very interesting point is service availability. That means, if we have many Matlab scripts providing the same service running on n different nodes in the network (and assume a stable QAD Grid server) then failure of up to n − 1 nodes still does not harm the availability of this service, since the QAD Grid server will distribute incoming jobs to the remaining running nodes.
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New Statistical Algorithms for the
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Contents Acknowledgments . . . . .
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New Statistical Algorithms for the
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Extended Abstract English Version M
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German Version Das Gebiet der Prote
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Chapter 1 Introduction and Survey 1
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1.2. GOALS, OBJECTIVES AND TASKS 7
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1.2. GOALS, OBJECTIVES AND TASKS 9
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Chapter 2 Preliminaries 2.1 Topic O
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2.1. TOPIC OVERVIEW 13 Figure 2.1.1
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2.1. TOPIC OVERVIEW 15 completeness
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2.2. AN EXAMPLE 17 (a) Opera A (b)
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2.2. AN EXAMPLE 19 Figure 2.2.6: Tw
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2.2. AN EXAMPLE 21 successes. We ca
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Chapter 3 Mathematical Modeling and
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3.2. INTRODUCTION TO MALDI TOF MS 2
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3.3. PREPROCESSING 31 mix (external
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3.3. PREPROCESSING 33 Figure 3.3.5:
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3.3. PREPROCESSING 35 Figure 3.3.7:
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3.4. HIGHLY SENSITIVE PEAK DETECTIO
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3.5. PEAK DETECTION IN 2D MAPS 43
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3.6. PEAK REGISTRATION (ALIGNMENT)
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3.7. IDENTIFYING POTENTIAL FEATURES
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3.8. EXTRACTING FINGERPRINTS 57 Fig
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3.8. EXTRACTING FINGERPRINTS 59 FID
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3.8. EXTRACTING FINGERPRINTS 61 Dim
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3.9. COMPLEXITY ANALYSIS 63 3.9 Com
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Chapter 4 (Bio-)Medical Application
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4.1. DATA USED 67 4.1.2 Serum Data
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4.2. STATISTICAL REMARKS 69 1. Vali
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4.2. STATISTICAL REMARKS 71 Molar v
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4.2. STATISTICAL REMARKS 73 � Fir
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4.2. STATISTICAL REMARKS 75 Let ˆ
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4.2. STATISTICAL REMARKS 77 the boo
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4.3. STUDY RESULTS 79 Figure 4.3.3:
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4.3. STUDY RESULTS 81 Figure 4.3.4:
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4.3. STUDY RESULTS 83 � kNN (gen.
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4.3. STUDY RESULTS 85 d(x, θi) =
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4.3. STUDY RESULTS 87 pairs of obje
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4.3. STUDY RESULTS 89 � “Peptid
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4.4. IDENTIFICATION OF PROTEOMIC FI
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4.6. BIOLOGICAL APPLICATIONS 101 4.
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Chapter 5 Computer Science Grid Str
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5.1. INTRODUCTION 105 � A node is
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Page 179 and 180: Appendix A Implementation Details T
Page 181 and 182: Appendix B Curriculum Vitae Name Ti
Page 183 and 184: References Aebersold, R. and Mann,
Page 185 and 186: REFERENCES 179 Breiman, L. (2001).
Page 187 and 188: REFERENCES 181 Downard, K. M. and M
Page 189 and 190: REFERENCES 183 Gillette, M. A., Man
Page 191 and 192: REFERENCES 185 Huyghe, E., Muller,
Page 193 and 194: REFERENCES 187 Kuijpens, J. L. P.,
Page 195 and 196: REFERENCES 189 McLachlan, S. M. and
Page 197 and 198: REFERENCES 191 Platt, J. C. (1999).
Page 199 and 200: REFERENCES 193 Stone, M. (1974). Cr
Page 201 and 202: REFERENCES 195 Washburn, M. P., Wol
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