| Line 16: | Line 16: | ||
**S1 = Dell Laptop Latitude E6320, Processor=P1, Memory = 8Gb, Storage=256Gb (SSD), Max power consumption=52W, Cost (time of purchase)~1200 euros ({{CREF|cb7e6b406491a11c:0d84339816de0271}}) | **S1 = Dell Laptop Latitude E6320, Processor=P1, Memory = 8Gb, Storage=256Gb (SSD), Max power consumption=52W, Cost (time of purchase)~1200 euros ({{CREF|cb7e6b406491a11c:0d84339816de0271}}) | ||
**S2 = Samsung Mobile Galaxy Duos GT-S6312, Processor=P2, Memory = 0.8Gb, Storage=4Gb, Battery=1300 mAh / 3.9V / up to 250 hours, Max power consumption~5W, Cost (time of purchase)~200 euros ({{CREF|cb7e6b406491a11c:a9740acbe06bcd1e}}) | **S2 = Samsung Mobile Galaxy Duos GT-S6312, Processor=P2, Memory = 0.8Gb, Storage=4Gb, Battery=1300 mAh / 3.9V / up to 250 hours, Max power consumption~5W, Cost (time of purchase)~200 euros ({{CREF|cb7e6b406491a11c:a9740acbe06bcd1e}}) | ||
| − | **S3 = Polaroid Tablet Executive 9" MID0927, Processor=P3, Memory=1Gb, Storage=16Gb, Battery=3500 mAh / 3.9V / up to 80 hours, Max power consumption~13W, Cost (time of purchase)~100 euros () | + | **S3 = Polaroid Tablet Executive 9" MID0927, Processor=P3, Memory=1Gb, Storage=16Gb, Battery=3500 mAh / 3.9V / up to 80 hours, Max power consumption~13W, Cost (time of purchase)~100 euros ({{CREF|cb7e6b406491a11c:3419444faf22f3d0}}) |
*Processors: | *Processors: | ||
**P1 = Intel Core i5-2540M, 2.60GHz, 2 cores ({{CREF|54cd38490124ef51:425ae4e3483c82e8}}) | **P1 = Intel Core i5-2540M, 2.60GHz, 2 cores ({{CREF|54cd38490124ef51:425ae4e3483c82e8}}) | ||
**P2 = Qualcomm MSM7625A FFA, ARM Cortex A5, ARMv7, 1 GHz, 1 core ({{CREF|54cd38490124ef51:ae17889f40209ae7}}) | **P2 = Qualcomm MSM7625A FFA, ARM Cortex A5, ARMv7, 1 GHz, 1 core ({{CREF|54cd38490124ef51:ae17889f40209ae7}}) | ||
| − | **P3 = Allwinner A20 (sun7i), Dual-Core ARM Cortex A7, ARMv7, 1.6GHz, Mali400 GPU, 2 core () | + | **P3 = Allwinner A20 (sun7i), Dual-Core ARM Cortex A7, ARMv7, 1.6GHz, Mali400 GPU, 2 core ({{CREF|54cd38490124ef51:fc020ce2e4d44f3d}}) |
| − | **P4 = NVidia Quadro NVS 135M, 16 cores, 400MHz () | + | **P4 = NVidia Quadro NVS 135M, 16 cores, 400MHz (TBD) |
*Processor mode: | *Processor mode: | ||
**W1 = 32 bit | **W1 = 32 bit | ||
| Line 32: | Line 32: | ||
**O2 = OpenSuse 12.1, Kernel 3.1.10, cost=free ({{CREF|c4d3ce728f46eea2:29ce89f1a1446e89}}) | **O2 = OpenSuse 12.1, Kernel 3.1.10, cost=free ({{CREF|c4d3ce728f46eea2:29ce89f1a1446e89}}) | ||
**O3 = Android 4.1.2, Kernel 3.4.0, cost=free ({{CREF|c4d3ce728f46eea2:e734c48d5a5824c1}}) | **O3 = Android 4.1.2, Kernel 3.4.0, cost=free ({{CREF|c4d3ce728f46eea2:e734c48d5a5824c1}}) | ||
| − | **O4 = Android 4.2.2, Kernel 3.3.0, cost=free () | + | **O4 = Android 4.2.2, Kernel 3.3.0, cost=free ({{CREF|c4d3ce728f46eea2:d3e9b97f6994444b}}) |
*Compilers: | *Compilers: | ||
Revision as of 10:34, 22 August 2014
Computational species "bw filter simplified less" (CID=45741e3fbcf4024b:1db78910464c9d05)
Notes
Some cost-aware experiments (execution time, size, energy, compilation time) performed by Grigori Fursin. It supports our research on continuous performance tracking, code optimization and compiler benchmarking (regression detection).
This computation species (kernel) is a threshold filter - it is used in image processing and neuron activation functions (part of artificial neural networks).
Used artifacts
- Datasets:
- D1 = grayscale image 1 (CID=8a7141c59cd335f5:c8848a1b1fb1775e), size=1536x1536~2.4E6 (pixels or neurons)
- D2 = grayscale image 2 (CID=8a7141c59cd335f5:0045c9b59e84318b), size=1536x1536~2.4E6 (pixels or neurons)
- Systems:
- S1 = Dell Laptop Latitude E6320, Processor=P1, Memory = 8Gb, Storage=256Gb (SSD), Max power consumption=52W, Cost (time of purchase)~1200 euros (CID=cb7e6b406491a11c:0d84339816de0271)
- S2 = Samsung Mobile Galaxy Duos GT-S6312, Processor=P2, Memory = 0.8Gb, Storage=4Gb, Battery=1300 mAh / 3.9V / up to 250 hours, Max power consumption~5W, Cost (time of purchase)~200 euros (CID=cb7e6b406491a11c:a9740acbe06bcd1e)
- S3 = Polaroid Tablet Executive 9" MID0927, Processor=P3, Memory=1Gb, Storage=16Gb, Battery=3500 mAh / 3.9V / up to 80 hours, Max power consumption~13W, Cost (time of purchase)~100 euros (CID=cb7e6b406491a11c:3419444faf22f3d0)
- Processors:
- P1 = Intel Core i5-2540M, 2.60GHz, 2 cores (CID=54cd38490124ef51:425ae4e3483c82e8)
- P2 = Qualcomm MSM7625A FFA, ARM Cortex A5, ARMv7, 1 GHz, 1 core (CID=54cd38490124ef51:ae17889f40209ae7)
- P3 = Allwinner A20 (sun7i), Dual-Core ARM Cortex A7, ARMv7, 1.6GHz, Mali400 GPU, 2 core (CID=54cd38490124ef51:fc020ce2e4d44f3d)
- P4 = NVidia Quadro NVS 135M, 16 cores, 400MHz (TBD)
- Processor mode:
- W1 = 32 bit
- W2 = 64 bit
- OSs:
- O1 = Windows 7 Pro SP1, cost~170 euros (CID=c4d3ce728f46eea2:10c4f7484446b689)
- O2 = O1, MinGW32
- O2 = OpenSuse 12.1, Kernel 3.1.10, cost=free (CID=c4d3ce728f46eea2:29ce89f1a1446e89)
- O3 = Android 4.1.2, Kernel 3.4.0, cost=free (CID=c4d3ce728f46eea2:e734c48d5a5824c1)
- O4 = Android 4.2.2, Kernel 3.3.0, cost=free (CID=c4d3ce728f46eea2:d3e9b97f6994444b)
- Compilers:
- X1 = GCC 4.1.1
- X2 = GCC 4.4.1
- X3 = GCC 4.4.4
- X4 = GCC 4.6.3
- X5 = GCC 4.7.2
- X6 = GCC 4.8.3
- X7 = GCC 4.9.1
- X8 = LLVM 3.1
- X9 = LLVM 3.3.2
- X10 = Open64 5.0
- X11 = PathScale 2.3.1
- X12 = NVidia CUDA Toolkit 5.0
- X13 = Microsoft Visual Studio 2013, cost = has free minimal version
- X14 = Intel Composer XE 2011, cost =
- Compiler optimization level:
- O1 = Performance (usually -O3)
- O2 = Size (usually -Os)
- Number of run-time code repetitions (for example, processing steps in neural networks):
- R1 = 4000
- R2 = 1000
- R3 = 400
- Total number of computations (processed neurons or pixels)
- T1 ~ 9.6E9
- T2 ~ 2.4E9
- T3 ~ 1.0E9
- Costs
- C1= Execution time
- C2 = Energy
- C3 = Code size
- C4 = Compilation time
- C5 = System size
- C6 = Hardware price
- C7 = Software price
- C8 = (Auto-)tuning price
- C9 = Development time
- C10 = Validation and testing time
Notes
Energy: 1Wh = 3600 joules
W = mAh * V / 1000 = 1300 * 3.9 / 1000 ~ 5W
