This information is confidential and was prepared by G-Cube solely for the use of our client and investor; it is not to be relied on by any 3rd party without G-Cube's prior written consent

Server Side Optimization for SSD (SSOS)

G-Cube Inc.

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Agenda

• Introduction

• OS’s storage stack

• Storage stack optimization for next-generation SSD

• Ongoing works

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• It’s time for low-latency I/O.-Very high I/O demand in computer systems. -Social network services, cloud platform ,and desktop users

• New technology - Storage Class Memory (SCM) -high-performance and robustness + the archival capabilities and low cost.

-Deployable as a disk drive replacement as well as a scalable main memory alternative.

- NAND Flash, Phase-change memory (PCM), MRAM, RRAM, …

Introduction

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There is No Free Lunch !

Storage Class Memory

Storage Stack in Modern OS

What matters us !Application performance: response time, throughput, TPM…

What SCM vendors give to us.High-throughput and ultra-latency

Show Different Perfor-mance Number !

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User Experience with New Computing Resources, New Service Framework, but Traditional System & Interfaces

User

System

Resource* Many-core CPU ( 64 cores ~) * No mechanical movement(~ microseconds)

Virtual Memory File systemBlock I/O & I/O schedulerDevice driverHW Interfaces

User Experience is poor

Poor

parallel

Unaware

fast

UX

Scalable

Resourceawarness

Performance

non-scalable

Parallism

Service Mobile apps, Cloud, Big data, HPC… I/O

pressure

Highly frequent & concurrent & randomized

Interfaces

Narrow and Limited Information

Device-aware

Scalable

Rich & Context-aware

Best

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SSD Performance in Modern Operating System

I/O load(users, applications)

Performance

HDD

SSD (UX)

SSD vendor performance

SSD

최대

성능

향상

2~4 배

Storage Stack in Modern OS

Show Differ-ent Perfor-mance Num-ber !

Storage Class Memory

What SCM ven-dors give to us.High-throughput and ultra-latency

What matters us !Application perfor-mance: response time, throughput, TPM…

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Technical strategy

Server Side Optimization for SSD: Make fast & broad I/O path between user & SSD

I/O 부하량(users, applications)

성능 (1/average response time)

SSD (now)

SSD (hardware)

SSD 최

대 성

능SSD 최대 부하

SSD ( 제안 제품 )

Fast I/O path

Broad I/O path

• User latency, response time

• Throughput

“Exploting peek device throughput even from con-

current small random access workload”

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Storage Stack in Modern OS

Device driver

IO Scheduler

Filesystem

Apps

Hard disk drive

Block layerLong and complex to mitigateperformance gap between CPU, memory and hard disk drive.

Diskified

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Storage Stack Optimizations for NVM SSD

Device driver

IO Scheduler

Filesystem

Apps

NVM SSD

Block layer

De-diskify Employ SSD feature Remove diskified feature in

software storage stack Enlarge interfaces between

layers Make I/O path synchronous

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Performance Metrics

•Latency-IOPS (I/Os per second)-Small random workload

•Throughput-Bandwidth (MB/s)

-Large sequential workload

•Our goal -Exploit peak device throughput from small random access workload.

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Focusing on Latency…

•New Hardware Abstraction Layer.-Get rid of disk-based HAL overhead.

•Polling-based interfaces-Avoid interrupt handling overhead for NVM SSD with a few micro-second response time.

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Polling vs Interrupt

I/O command

I/O command IRQ SoftIRQ

ApplicationContext

ApplicationContext

sleep

Device response

time

Device response

time

Busy wait(polling)

Interrupt-based I/O event handling

Polling-based I/O event handling

Schedule delay

4~5us 8~15us

2~3us

4~5us

14~23us

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Performance Evaluation

Peak device throughput

polling

sequential read seq_write random read rand_write

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Device Throughput

•Temporal merge-Device-level scatter-gather I/O

•Multiple I/O path-Non-blocking write

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Temporal Merge vs Back/Front Merge

Temporal merged requestSingle I/O

StorageAddressSpace

HostMemory

Front/back merged No merged

Temporal merged into a single request

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Merge I/O requests from temporal locality

REQ 1

Temporal Merge

CPU0 CPU1 CPU2 CPU3 CPU4 CPU5 CPU6 CPU7

5

4

3

3

2

5

4

3

1 2

1

5

4

3

2

1 1 1 1 11

Read requests

2 22 2

REQ 2

REQ 3

REQ 4

Busy Wait

Perform I/O

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Performance Evaluation

Peak Device Throughput

Temporal Merge

sequential read seq_write random read rand_write

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Multiple I/O Path

• For handling the buffered write from filesystem.

CPU0 CPU1 CPU2 CPU3 CPU4 CPU5 CPU6 CPU7

5

4

3

3

2

5

4

3

1 2

1

3

2 22 2

1 1 1 11

E

D

C

B

A

Flush thread

Scheduler A B D EC

BufferedWrite

(asynchronous)

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Performance Evaluation

19

ideal max throughput

All done except for random read!!!

Misunderstood read-ahead contextin EXT2 filesys-tem

sequential read seq_write random read rand_write

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Latency Again…

• Pipelined post I/O processing-Post I/O processing: DMA unmap, notifying I/O comple-tion, free pages.

-More effective for small-size requests.

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Queuing & Pipelined Post I/O Processing

old

Temporal merged pages to do I/O

PCI DMA transfer

DMA DONEPost processingtime for n pages(serialized)

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Pipelined Post I/O Processing

22

Pipelined Post I/O Processing

Temporal merged pages to do I/O

DMA DONE

Post processing time for a page (pipelined)

BLOCK I/O Done(new interfaces)

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Performance Evaluation

23

Seq.Read Seq.Write Rand.Read Rand.Write Rand.R/W(7/3)0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

FIO, 512 byte, 16 threadsincrease 14% (60,000 IOPS)

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Issue: Scalable I/O for Many-core Machine

Device driver

IO Scheduler

Virtual Memory(page cache tree)

Many-core CPU

NVM SSD

Block layer

1 2 4 8 16 320

2000400060008000

10000120001400016000

initail writers

rewriters

readers

re-readers

random readers

random writers

# of threads

Thro

ughp

ut (M

B/se

c)

Ramdisk buffered read

1 2 4 8 16 320

5000

10000

15000

20000

25000

30000

initail writersrewritersreadersre-readersrandom readersrandom writers

# of threads

Thro

ughp

ut (M

B/se

c)

PCI-E SSD buffered read

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Scalable Lock for Many-Core CPU

• Access radix tree in Linux kernel- For adding/removing/updating a page into the tree.- spin_lock_irq : tree_lock. non-scalable.

• Possible approaches for scalable lock- Less contented lock structure.

8 With help of application hint. 8 Finer-grained lock 8 With sacrifice of memory resource.

- Remote Core Locking8 No cache invalidation storm.

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Conclusion

User applications

Broad & Thin I/O stack for NVM SSD

NVM SSD

High throughput (Broad way)

Low-latency(Thin layer)

Extended inter-faces for NVM

SSD

Software Storage Stack Optimizations for Next-generation SSD

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References

• Young Jin Yu, Dong In Shin, Woong Shin, Nae Young Song, Jae Woo Choi, Hyeong Seog Kim, Hyeonsang Eom, Heon Young Yeom, Optimizing the Block I/O Subsystem for Fast Storage Devices, ACM Transactions on Computer Sys-tems (TOCS), Volume 32 Issue 2, June 2014, Article No. 6

• Dong In Shin, †Young Jin Yu, †Hyeong S. Kim, Jae Woo Choi,Do Yung Jung, †Heon Y. Yeom, "Dynamic Interval Polling and Pipelined Post I/O Processing for Low-Latency Storage Class Memory", USENIX HotStorage 2013, San Jose, June 26-28, 2013

• Jae Woo Choi, Young Jin Yu, Hyeonsang Eom,Heon Young Yeom, Dong In Shin, "SAN Optimization for High Performance Storage with RDMA Data Trans-fer", 7th parallel data storage workshop (PDSW) in conjunction with SC'12, Saltlake City, USA, 10-16 Nov. 2012

• Young Jin Yu, Dong In Shin, Woong Shin, Nae Young Song, Hyeonsang Eom, and Heon Young Yeom, "Exploiting Peak Device Throughput from Random Ac-cessWorkload", USENIX HotStorage 2012, Boston, USA, 13-14 Jun. 2012