Quantifying the Energy Cost of Data Movement for Emerging Smartphone Workloads on Mobile Platforms

Transcription

1 Quantifying the Energy Cost of Data Movement for Emerging Smartphone Workloads on Mobile Platforms Arizona State University Dhinakaran and Carole-Jean ) October 28, 2014 IEEE International Symposium on Workload Characterization

2 Smartphones 2GB RAM, 8 cores, full HD display, hardware accelerators Space and weight constraints limit battery capacity Need to reduce power consumption wherever possible Can consume power even as high as 3 Watts during regular use 2

3 Data movement is a power hog! Power Consumption (W) Time LOAD - without data dependency LOAD - with data dependency ADD- single issue ADD - dual issue LOADs consume more power than ADDs 3

4 Memory hierarchy SoC components share data through the DRAM Cortex-A9 I/DTLB L1I$ L1D$ Cortex-A9 I/DTLB L1I$ L1D$ Cortex-A9 I/DTLB L1I$ L1D$ Cortex-A9 I/DTLB L1I$ L1D$ Cache Snoop Controller AXI Interface L2 Unified Cache LPDDR Memory GPU DSP ISP... 4

5 Problem statement How do we measure the data movement energy on a commercial smart phone device? What is the impact of data movement energy on the total energy consumption for smart phone workloads? 5

6 Presentation Outline Introduction Methodology Experimental setup Energy measurement for workloads Results Conclusion 6

7 Data movement characterization Identify the types of data movement Finding the associated energy costs Characterize representative workloads Register L1 cache L2 cache Memory E mem E L2 E L1 E reg 7 ΔEnergy L1 ΔEnergy L2 ΔEnergy mem

8 Micro-benchmarks 1.E DRAMtoReg REG 2. E L2toReg 3. E L1toReg 4. E L1toReg, no- dep 5. E Add 6. E NOP L1 $ L2 $ DRAM 8

9 Micro-benchmarks 1. E DRAMtoReg REG 2.E L2toReg 3. E L1toReg 4. E L1toReg, no- dep 5. E Add 6. E NOP L1 $ L2 $ DRAM 9

10 Micro-benchmarks 1. E DRAMtoReg REG 2. E L2toReg 3.E L1toReg 4.E L1toReg, no- dep 5. E Add 6. E NOP L1 $ L2 $ DRAM 10

11 Data movement micro-benchmark design Initialize() Benchmark template for(i = 1 iterations/x) do end for Cleanup(). <operation> // repeat x times.. Data movement Initialize(): allocate mem, initialize array, start timer Operation : ptr = *(void **)ptr; Cleanup() : free memory, end timer c = &d x = &a b = &c a = &b 11

12 Programming concerns Prefetching Data locality Compiler optimization Loop unrolling Task priority Task migration CPU frequency governor 12

13 Stall cycle energy Pointer chasing causes data dependencies Processor stalls when waiting for data from memory E.g. 3 stall cycles for each load in E L1toReg E stall = (E L1toReg E L1toReg,no- dep )/N stalls Eliminating stall cycle energy E L2toReg = (E L2toReg- total N stalls *E stall )/N mem- accesses 13

14 Experimental platform Samsung Galaxy S3 I9300 smart phone Exynos SMDK 4412 Quad 4 ARM Cortex A9 cores L1 I cache 32 KB L1 D cache 32 KB L2 shared cache 1 MB DRAM 1GB Android 4.3 (Jelly Bean) Kernel configured to access performance counters 14

15 Experimental setup A DAQ is used to measure current drawn by the smartphone NI SignalExpress records voltage, current, power 15

16 Energy measurements P benchmark = P daq- reading P idle E benchmark = P benchmark.dt 16 Benchmark Idle 0 ΔBenchmark ΔIdle

17 Energy cost of data movement Operation Energy cost(nj) Energy(nJ) NOP ADD LOAD L1 Reg LOAD L2 Reg LOAD RAM Reg Stall cycle Moving data could cost 115 times as much as an ADD operation 17

18 Microbenchmark validation Compare the measured energy with the values estimated from the data movement energy costs Energy est. = N L1 * E L1toReg + N L2 *E L2toL1 + N DRAM *E DRAMtoL2 + N ADD *E ADD + N NOP *E NOP Error rate (%) ADD + L1 NOP + L1 NOP + L1 + ADD ADD + L2 ADD + RAM Avg. error rate is 3.4%, Max. error rate is 8.6% 18

19 Smartphone workloads Educational Web Browsing(EWB) * Models reading documents along with browsing Video playback * HD video Photo Viewing * High resolution images General Web Browsing (GWB) * Offline, automated benchmark Realistic General Web browsing (R-GWB) * Represents realistic browsing behavior (scroll-up, horizontal scroll and random delays) Frozen Bubble Interactive game * Pandiyan et al., MobileBench IISWC 2013; Gutierrez et al., Bbench IISWC 11; Huang et al., Moby ISPASS 14 19

20 Profiling workloads Hardware Performance Counters CPU- Instruction count, L1 misses, L1 accesses, stalls etc. L2 cache controller L2 misses, L2 accesses, prefetches 20 ARM DS-5 Streamline

21 Total energy breakdown Device Energy Breakdown 100% 80% 60% 40% 20% 0% Data Movement Stalls Others 35.6% of the energy is spent in data movement on average 23.5% of the energy is spent during stall cycles 21

22 Data movement energy breakdown L1 -> Reg L2 -> L1 Data L2 -> L1 Instruction Mem -> L2 Prefetches GWB VideoPlayback PhotoView 39% 27% 47% 30% 33% 24% 3% 18% 10% 3% 30% 7% 21% 6% 2% 22

23 Data movement energy breakdown L1 -> Reg L2 -> L1 Data L2 -> L1 Instruction Mem -> L2 Prefetches EWB-Blackboard RWB Frozen Bubble 28% 44% 31% 39% 29% 37% 15% 10% 3% 16% 11% 3% 24% 8% 2% 23

24 Problem statement How do we measure the data movement energy on a commercial smart phone device? What is the impact of data movement energy on the total energy consumption for smart phone workloads? 24

25 Conclusion This is the first work that proposes a detailed methodology for quantifying the instruction-level data movement energy cost in modern smartphones The detailed data movement energy characterization shows 35.5% of total energy consumption is spent in data movement for interactive mobile workloads 24.5% of total energy consumption is spent during stall cycles 25

26 Quantifying the Energy Cost of Data Movement for Emerging Smartphone Workloads on Mobile Platforms Dhinakaran Pandiyan and Carole-Jean Wu Thank you! This work was partially supported by the NSF I/UCRC Center for Embedded Systems (NSF grant # ) and by Science Foundation of Arizona under the Bisgrove Early Career Scholarship. 26