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Transcription:

The Why and How of SSD Performance Benchmarking Esther Spanjer, SMART Modular Easen Ho, Calypso Systems

SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA. Member companies and individual members may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced in their entirety without modification The SNIA must be acknowledged as the source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. Neither the author nor the presenter is an attorney and nothing in this presentation is intended to be, or should be construed as legal advice or an opinion of counsel. If you need legal advice or a legal opinion please contact your attorney. The information presented herein represents the author's personal opinion and current understanding of the relevant issues involved. The author, the presenter, and the SNIA do not assume any responsibility or liability for damages arising out of any reliance on or use of this information. NO WARRANTIES, EXPRESS OR IMPLIED. USE AT YOUR OWN RISK. 2

Abstract A variety of parameters can influence the performance behavior of a solid state drive: current and previous workloads, fragmentation, block size, read/write mix, and queue depth to name a few SNIA s Performance Test Specification allows for performance benchmarking that result in repeatable and consistent test results This presentation will provide an overview of the SNIA SSD Performance Test Specification for both client and enterprise SSDs 3

Definition of SSS SSS = Solid State Storage 4

The Performance Landscape MB/s or Mb/s? IOPS? Block Size? Random or Sustained? Random Precondition Sustained Speed? Up to? 5

Variables influencing Performance Platform Test Hardware (CPU, interface, chipset, etc) Software (OS, drivers) SSS Device Architecture Flash geometry, cache, flash management algorithm, etc 6

Variables influencing Performance Platform Test Hardware (CPU, interface, chipset, etc) Software (OS, drivers) SSS Device Architecture Flash geometry, cache, flash management algorithm, etc Workload 1. Write history & preconditioning: State of device before testing 7

The need for Preconditioning Performance States for Various SSDs Normalized IOPS (IOPS/Max(IOPS)) 1.2 1 0.8 0.6 0.4 FOB NM (MLC) NS (SLC) JS (SLC) PSM (MLC) JM (MLC) Transition Steady State (desirable test range) 0.2 0 Santa Clara, CA USA August 2009 0 50 100 150 200 250 300 Time (Minutes) 8

Write History - 1 4K Random to 128K Sequential Transition 45000 40000 F.O.B. (~1hr) IOPS 35000 30000 25000 20000 15000 10000 5000 0 4K Steady State Random to Sequential Transition (~1.5hr) 128K Steady State 20 60 100 140 180 220 260 300 340 380 420 460 500 540 580 620 660 700 740 780 820 860 900 940 980 1020 1060 1100 1140 1180 1220 1280 1320 1360 1400 1440 1480 1520 1560 1600 1640 1680 Time (Minutes)

Write History - 2 4500 128K Sequential to 4K Random Transition 4000 3500 3000 2500 2000 1500 F.O.B. ~10 hrs 4K Steady State 1000 500 128K Steady State 0 20 60 100 140 180 220 260 300 340 380 420 460 500 540 580 620 660 700 740 780 820 860 900 940 980 1020 1060 1100 1140 1180 1220 1280 1320 1360 1400 1440 1480 1520 1560 1600 1640 1680 10

Variables influencing Performance Platform Test Hardware (CPU, interface, chipset, etc) Software (OS, drivers) SSS Device Architecture Flash geometry, cache, flash management algorithm, etc Workload 1. Write history & preconditioning: State of device before testing 2. Workload pattern: Read/write mix, transfer size, sequential/random 11

Workload Pattern 4000.0 3500.0 3000.0 2500.0 3D IOPS Surface Profile 0.0-500.0 500.0-1000.0 1000.0-1500.0 1500.0-2000.0 2000.0-2500.0 2500.0-3000.0 3000.0-3500.0 3500.0-4000.0 Performance depends on Read/Write Mix Block Size Queue Depth (not shown) IOPS 2000.0 1500.0 1000.0 500.0 0.0 0/100 10/90 20/80 35/65 65/35 80/20 90/10 0.5 100/0 2 8 1024 Block Size (KB) R/W Mix (%) 12

Variables influencing Performance Platform Test Hardware (CPU, interface, chipset, etc) Software (OS, drivers) SSS Device Architecture Flash geometry, cache, flash management algorithm, etc Workload 1. Write history & preconditioning: State of device before testing 2. Workload pattern: Read/write mix, transfer size, sequential/random 3. Data Pattern: The actual bits in the data payload written to the device 13

Dependency on data content - 1 3D IOPS Surface Profile (IOMETER 2008) 30,000-35,000 25,000-30,000 20,000-25,000 15,000-20,000 10,000-15,000 5,000-10,000 0.5 0-5,000 2 8 Transfer Size (KB) 32 128 512 100/0 90/10 80/20 65/35 35/65 R/W Mix % 0/100 10/90 20/80 5,000 0 IOPS 35,000 30,000 25,000 20,000 15,000 10,000 14

Dependency on data content - 2 3D IOPS Surface Profile (IOMETER 2008) IOMeter 2008 Low Entropy Data Content 30,000-35,000 25,000-30,000 20,000-25,000 15,000-20,000 10,000-15,000 5,000-10,000 0-5,000 15 0.5 1 2 4 8 16 32 Transfer Size (KB) 3D IOPS Surface Profile (IOMETER 2006) 64 128 256 512 1024 100/0 90/10 80/20 65/35 35/65 30,000-35,000 25,000-30,000 20,000-25,000 15,000-20,000 10,000-15,000 5,000-10,000 0-5,000 20/80 0.5 R/W Mix % 2 Transfer Size (KB) 10/90 0/100 5,000 0 IOPS 35,000 30,000 25,000 20,000 15,000 10,000 8 32 128 512 100/0 90/10 80/20 65/35 0/100 10/90 20/80 35/65 R/W Mix % IOMeter 2006 High Entropy Data Content 5,000 0 IOPS 35,000 30,000 25,000 20,000 15,000 10,000 15

The Need for Industry Standardization! SNIA Technical Working Group (TWG) Created in early 2009 Specification for tests procedures to enable comparative testing of SSS performance Agnostic Does not favor any one technology Relevant & Repeatable Meaningful to end users Practical Complete with reasonable time and effort Performance Test Spec (PTS) 1.0 Client Released PTS 1.0 Enterprise Released PTS 1.1 in progress, target release 4Q11 16

Benchmark Types Synthetic IOMeter, VDBench Applicationbased SysMark, PCMark Trace-based Storage Bench Test specific scenario (QD, block size, transfer rate) Good to determine corner case behavior Test performance of specific application (ignores QD, transfer size, etc.) Illustrates real world differences Measures performance as drive is used (traces) Most valid when similar applications are run (no two user workloads are the same) SNIA PTS focuses on synthetic based benchmark tools 17

SSSI Reference Test Platform Intel S5520HC Single Intel W5580, 3.2GHz, Quad-core CPU 12GB, 1333MHz, ECC DDR3 RAM CentOS 5.5 Calypso RTP Backend V1.5 Calypso Test Suite (CTS) V6.5 LSI 9212-4e4i 6Gb/s SAS HBA Intel ICH10R 3Gb/s SATA 8X Gen-II PCI-e 18

Tests Contained In Draft V1.0 Spec. The V1.0 Specification encompasses: A suite of basic SSS performance tests Write Saturation Random Access R/W: 100% Writes BS: 4K Enterprise IOPS Random Access R/W: 100/0, 95/5, 65/35, 50/50, 35/65, 5/95, 0/100 BS: 1024K, 128K, 64K, 32K, 16K, 8K, 4K, 0.5K Enterprise TP Sequential Access R/W: 100/0, 0/100 BS: 1024K, 64K, 8K, 4K, 0.5K Enterprise Latency Random Access R/W: 100/0, 65/35, 0/100 BS: 8K, 4K, 0.5K Preconditioning and Steady State requirements Standard test procedures Standard test reporting requirements 19

What Is NOT Covered In the Spec Application workload tests Matching to user workloads Energy efficiency Required test platform (HW/OS/Tools) Certification Device endurance, availability, data integrity - Performance Test Specification v1.0 Section 1.4 20

Basic Test Flow 1. Purge 2. Set Conditions 3. Pre-Condition 4. Run Until SS 5. Collect Data Security Erase, Sanitize, Format Unit, other proprietary methods where indicated Set user selectable test parameters, such as Active Range, Data Pattern, Demand Intensity Workload independent (WIPC) Workload dependent (WDPC) Reiterate loops until Steady State is reached, or run to a prescribed maximum number of loops Collect data from Steady State Measurement Window 6. Generate Reports Use standard report formats and include required and optional elements 21

Key Concepts Used in the Spec. A. Purge B. Pre-Condition Workload independent Workload dependent C. Active Range Pre-conditioning Test D. Steady State Measurement window Data excursion condition Slope excursion condition 22

A: Purge As per the PTS V1.0 Specification, purge is defined as: The process of returning an SSS device to a state in which subsequent writes execute, as closely as possible, as if the device had never been used and does not contain any valid data Example implementation includes: ATA Security Erase, Sanitize, SCSI Format Unit 23

B: Pre-Conditioning Pre-Conditioning is a key requirement in getting repeatable, representative results Goal is to put drive into Steady State, using: Workload independent PTS v1.0 Section 3.3 Use a prescribed workload unrelated to the test loop Write 2X user capacity using SEQ/128KiB blocks Workload dependent PTS v1.0 Section 3.3 Run test workload itself as pre-conditioning (self preconditioning) 24

C: Active Range As per the PTS V1.0 Specification, Active Range is defined as: ActiveRange is the range of LBA s that may be accessed by the preconditioning and/or test code... They are normally defined as % of the maximum LBA available to the user Note Pre-conditioning and Test can have different Active Ranges 25

D: Steady State Definition Premise is that reported data should be take only AFTER the test loop results shows the drive has reached and maintained Steady State The Measurement Window is the interval, measured in Rounds, when the test results have entered and maintained Steady State for 5 Rounds 26

D: Steady State Definition Steady State is reached only if BOTH of the following conditions are satisfied (assuming y is the variable being tracked): 1. Variation of y within the Measurement Windows is within 20% of the Average Max(y)-Min(y) within the Measurement Window is no more than 20% of the Ave(y) within the Measurement Window; and 2. Trending of y within the Measurement Windows is within 10% of the Average [Max(y) as defined by the linear curve fit of the data within the Measurement Window] [Min(y) as defined by the best linear curve fit of the data within the Measurement Window] is within 10% of Ave(y) within the Measurement Window. 27

D: SS Measurement Window 28

D: SS Measurement Window 29

D: Steady State Definition Compare Note [Data Excursion] with [20% of Average] [Slope Excursion] with [10% of Average] This method is slightly more tolerant than +10% and 10% data excursion method and +5% and 5% slope excursion method 30

D: How Good is the Steady State 31

Workload Schematics Write Saturation Enterprise IOPS Enterprise TP Enterprise Latency Random Access R/W: 100% Writes BS: 4K Random Access R/W: 100/0, 95/5, 65/35, 50/50, 35/65, 5/95, 0/100 BS: 1024K, 128K, 64K, 32K, 16K, 8K, 4K, 0.5K Sequential Access R/W: 100/0, 0/100 BS: 1024K, 64K, 8K, 4K, 0.5K Random Access R/W: 100/0, 65/35, 0/100 BS: 8K, 4K, 0.5K 32

IOPS RW/BS Sequence 33

Enterprise IOPS RW/BS Sequence 34

TP RW/BS Sequence 35

Enterprise Latency RW/BS Sequence 36

Example: Enterprise IOPS DUT: 100GB-Class Enterprise SLC drive Test Parameters: Active Range = [0,100%] Thread Count=2 Queue Depth (Outstanding IO/Thread)=16 DP=RND 37

Enterprise IOPS Draft Formatted Report, 1/6 38

Enterprise IOPS Draft Formatted Report, 2/6 39

Enterprise IOPS Draft Formatted Report, 3/6 40

Enterprise IOPS Draft Formatted Report, 4/6 41

Enterprise IOPS Draft Formatted Report, 5/6 42

Enterprise IOPS Draft Formatted Report, 6/6 43

PTS Follow-On Work (PTS-E 1.1) Idle Recovery See how the drive responds to host idle time amidst continuous access Cross Stimulus Recovery See how drive handles switching between sustained access patterns Demand Intensity See how drive responds to increasing host demands Response Time Histogram Get detailed response time statistics during specific stimulus IOPS/W Measures power efficiency of the device Trace-Based Workloads Enterprise Composite Synthetic Workload Captures or uses captured workloads traces and provide a consistent way to playback such traces Synthetic composite workload for Enterprise environments similar to JEDEC workload for endurance testing 44

Q&A / Feedback Please send any questions or comments on this presentation to SNIA: tracksolidstate@snia.org Eden Kim Easen Ho Esther Spanjer Many thanks to the following individuals for their contributions to this tutorial. - SNIA Education Committee 45