evaluating servers using iso-metrics: power, performance...

Evaluating Servers using Iso-Metrics:Power, Performance and Programmability Implications

Dimitrios S. Nikolopoulos

School of Electronics, Electrical Engineering and Computer ScienceQueen’s University of Belfast

January 20, 2015

D. Nikolopoulos (EEECS@QUB) Iso-Metrics, MULTIPROG’15 January 20, 2015 1 / 30

Motivation: Servers and Micro-Servers

Outline

1 Motivation: Servers and Micro-Servers

2 Micro-servers for Real-Time Analytics

3 Metrics

4 Field Tests

5 Programmability and Iso-Effort

6 Conclusion



Diversity in the Server Landscape



Diversity in Workloads



Diversity in Software



How do we choose the right server?


Micro-servers for Real-Time Analytics

Outline



3 Metrics

4 Field Tests


6 Conclusion



NanoStreams Hardware in a Nutshell



NanoStreams Software in a Nutshell



Showcase: Real-Time Option Pricing

Not particularly compute- or data-intensive, low-latency anddata-parallel workloads

Monte Carlo, Black Scholes, Binomial PricingInstance runs in ms or µs, must complete before next tradeHeavily traded symbols trigger Koptions/session

Price = (−1)p(SN((−1)pd1)− Pe−rTN((−1)pd2)

)(1)

Price =e−rT

N

N∑i=1

max

(0,S − Pe(r−σ2

2)T+σ

√Txi

)(2)

u = eσ√T and d =

1

u(3)


Metrics

Outline



3 Metrics

4 Field Tests


6 Conclusion


Metrics

Platform Agnostic Metrics1

Joules/option: Provider-side, sustained throughout trading day,reduction translates to less TCO

Time/option: User-side, end-to-end latency.

QoS: Calculating option before new price arrives; unknown deadline.

1Charles Gillan, Dimitrios S. Nikolopoulos, Giorgis Georgakoudis, Richard Faloon,George Tzenakis and Ivor Spence: On the Viability of Micro-Servers for FinancialAnalytics, In:WHPCF’14–SC’14, New Orleans, LA.


Metrics

QoS in Detail

0

10

20

30

40

50

60

70

80

90

100

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

% s

ucc

ess

pri

cing

(all-

or-

noth

ing

)

Processing time (s)

FB 7th July 2014

QoSCumulative Poisson (λ=8, bin-size=0.25)

0

10

20

30

40

50

60

70

80

90

100

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

% s

ucc

ess

pri

cing

(all-

or-

noth

ing

)

Processing time (s)

GOOG 15th July 2014

QoSCumulative Poisson (λ=8, bin-size=0.25)

Cumulative frequency distribution of Facebook and Google stock priceupdates for full trading sessions on July 7th and 15th 2014


Metrics

Iso-QoS and Energy

5.5

6

6.5

7

7.5

8

0 20 40 60 80 100 120 140

Inst

anta

neous

CPU

Pow

er

(Watt

s)

Time (Seconds)

QoS(t) = 1− e−λt∑

i=0

λt

bt!c(4)

G ≥ Nopt × Sopt (5)

Egap = Nopt × Jopt (6)

Ngaps = bY × session updates)c(7)

EQoS=Y = Ngaps × Egap (8)


Field Tests

Outline



3 Metrics

4 Field Tests


6 Conclusion


Field Tests

Experimental Platforms

Intel x86-64 Sandy Bridge, 2×octo-core Xeon E5-2650 @2.00GHz,32GB of DRAM (4 × 8GB DDR3 @1600Mhz), Linux CentOS 6.5,2.6.32 (2.6.32 431.17.1.el6.x86 64).

Xeon Phi (Knights Corner) 5110P model over PCIe. Sixty, 4-wayhyperthreaded cores, 512-bit vector unit. 6 GB GDDR5 DRAM. 1.053GHz. Linux kernel 2.6.38.8+mpss3.2.1.

Viridis 2U rack-mounted server with 16 microservers, 10 Gb/sEthernet. Calxeda EnergyCore ECX-1000, 4 ARM [email protected], 4 GB DRAM, Ubuntu 12.04 LTS.


Field Tests

Feed Handling

Financial trace data measurement setup


Field Tests

Power Measurement

PSU VRM CPU

PRE-PSU PRE-VRM

The path of the current supply to the CPU showing points at which wemeasured power. PSU is the power supply unit and VRM the voltage

regulator module.


Field Tests

Rankings in Monte Carlo

Table : MC kernel (N=0.5M and QoS=10%)

Platform VEC TYPE S/Opt J/Opt Energy(KJ)

Viridis(16×4×1) INTRINSICS 0.0038 0.3830 239.85

Intel(2×8×1) AUTOVECT 0.0044 0.3794 237.58

Xeon Phi(1×60×1) KNC512 0.0046 0.2234 139.92

Xeon Phi(1×60×2) NOVECT 0.0036 0.1856 116.26

Xeon Phi(1×60×4) INTRINSICS 0.0030 0.1584 99.19


Field Tests

Rankings in BT

Table : BT kernel (N=4000 and QoS=80%)


Intel(2×8×1) AVX256 0.0007 0.0611 306.49

Viridis(16×4×1) NEON128 0.0006 0.0603 302.41

Intel(1×8×1) INTRINSICS 0.0013 0.0527 264.32





Field Tests

Rankings in BT








Xeon Phi(1×60×2) KNC512 0.0005 0.0139 69.76


Field Tests

BT Ranking





Xeon Phi(1×60×2) AUTOVECT 0.0007 0.0281 140.84


Xeon Phi(1×60×1) KNC512 0.0007 0.0216 108.28


Field Tests

Servers vs. Micro-Servers

0

200

400

600

800

1000

1200

1400

1600

Intel(2×8×1) Viridis(16×4×1)

En

ergy

(KJ)

400050007000

BT kernel energy consumption scaling (at QoS=80%) of Viridis(16×4×1)and Intel(2×8×1)


Programmability and Iso-Effort

Outline



3 Metrics

4 Field Tests


6 Conclusion



Does programmability matter?

VEC TYPE Description

AVX256 Assembler code using AVX 256-bit instructions on the Intel Sandyridge.

INTRINSICS Compiler supplied C functions (ARM 128-bit, Intel 256-bit, Phi 512-bit)

KNC512 Assembler code for 512-bit vector instruction set on the Xeon Phi.

NEON128 Assembler code for the ARM NEON 128-bit unit.

AUTOVECT Compiler auto-vectorisation on all platforms



Vectorization Paradoxes




Intel(1×8×1) AVX256 0.0052 0.2526 632.95


Xeon Phi(1×60×2) AUTOVECT 0.0007 0.0281 70.42


Xeon Phi(1×60×1) KNC512 0.0007 0.0216 54.14



Vectorization Details

xi = axi + bxi+1 (9)

Vectorization of Binomial Tree kernel

Manual unrolling

Inter-lane shift to reducememory loads

Critical ISA differences betweenXeon and ARM


Conclusion

Outline



3 Metrics

4 Field Tests


6 Conclusion


Conclusion

Conclusions

Understanding efficiency through platform-agnostic metrics andmethodologies

Iso-metrics are useful for fair ranking of algorithms, architectures,systems

Mathematical formulation of new QoS metric for streaming analyticsIso-QoS for fair ranking of servers

Important applications: capacity planning, resource throttling, costminimisation, profit maximisation, ...

The programmability dilemma: Iso-effort vs. best effort


Conclusion

Credits

EU FP7 Grant 610509, EPSRC Grants L000055/1, L004232/1

Who does the hard work: Charles Gillan, Giorgis Georgakoudis,George Tzenakis, Ahmed Sayed, Ivor Spence, Richard Faloon, andNanoStreams technical team.


evaluating servers using iso-metrics: power, performance...

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