Download pptx - Shredder GPU-Accelerated Incremental Storage and Computation Pramod Bhatotia §, Rodrigo Rodrigues §, Akshat Verma ¶ § MPI-SWS, Germany ¶ IBM Research-India

ShredderGPU-Accelerated Incremental

Storage and Computation

Pramod Bhatotia§, Rodrigo Rodrigues§, Akshat Verma¶

§MPI-SWS, Germany¶IBM Research-India

USENIX FAST 2012

Pramod Bhatotia 2

Handling the data deluge• Data stored in data centers is growing at a fast

pace

• Challenge: How to store and process this data ?• Key technique: Redundancy elimination

• Applications of redundancy elimination• Incremental storage: data de-duplication• Incremental computation: selective re-execution

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Redundancy elimination is expensive

For large-scale data, chunking easily becomes a bottleneck

Content-based chunking [SOSP’01]

ContentMarker

ChunkingFile Chunks Hash Yes

No

Duplicate

Hashing Matching

0 1 0 1 1 0 1 0 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 0 1 0 1 0

FileFingerprint

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Accelerate chunking using GPUs

GPUs have been successfully applied to compute-intensive tasks

Multicore GPU based design0

0.5

1

1.5

2

2.5

3

GBp

s

2X

20Gbps (2.5GBps)

Using GPUs for data-intensive tasks presents new challenges

?

StorageServers

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Rest of the talk

• Shredder design• Basic design• Background: GPU architecture & programming

model• Challenges and optimizations

• Evaluation• Case studies

• Computation: Incremental MapReduce• Storage: Cloud backup

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Shredder basic design

GPU (Device)CPU (Host)

Reader

Transfer

Store

Chunkingkernel

Data for chunking

Chunkeddata

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GPU architecture

CPU(Host)

GPU (Device)

Multi-processor N

Multi-processor 2Multi-processor 1

SP SP SPSP

PCI

Host memory

Device global

memory

Shared memory

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GPU programming model

CPU(Host)

GPU (Device)

Multi-processor N

Multi-processor 2Multi-processor 1

SP SP SPSP

PCI

Host memory

Device global

memory

Shared memory

Input

Output

Threads

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Scalability challenges

1. Host-device communication bottlenecks

2. Device memory conflicts

3. Host bottlenecks(See paper for details)

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Host-device communication bottleneck

Challenge # 1

GPU (Device)

Device global

memoryPCI

Synchronous data transfer and kernel execution • Cost of data transfer is comparable to kernel

execution• For large-scale data it involves many data transfers

CPU (Host)

Mainmemory Chunking

kernel

I/O

Transfer

Reader

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Asynchronous execution

TimeCopy to Buffer 1

GPU (Device)Asynchronous

copy

Buffer 1

Device global memoryMain

memoryTransfer Buffer 2

CPU (Host)

ComputeBuffer 2

ComputeBuffer 1

Copy to Buffer 2

Pros: + Overlaps communication with computation+ Generalizes to multi-buffering

Cons:- Requires page-pinning of buffers at host side

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Circular ring pinned memory buffers

GPU (Device)

CPU (Host)

Memcpy

Pinned circular Ring buffers

Pageablebuffers

Device global memory

Asynchronouscopy

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Device memory conflictsChallenge # 2

GPU (Device)

Device global

memoryPCI

CPU (Host)

Mainmemory Chunking

kernel

I/O

Transfer

Reader

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Accessing device memory

Thread-1 Thread-2 Thread-4


Multi-processor

SP-1 SP-2 SP-3 SP-4

Thread-3400-600Cycles

Fewcycles

Device shared memory

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Un-coordinated accesses to global memory lead to a large number of memory bank conflicts

Memory bank conflicts

Multi-processor

SP


SP SP SP

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Accessing memory banks

Bank 0 Bank 3Bank 1 Bank 2

048

15

26

37

MSBs LSBs

Address

Memoryaddress

Chipenable

OR

Data out

Interleaved memory

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Memory coalescingDevice global memory


Memorycoalescing

Tim

e1 2 4Thread # 3

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Processing the data

Thread-1 Thread-2 Thread-4


Thread-3

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Outline

• Shredder design• Evaluation• Case-studies

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Evaluating Shredder

• Goal: Determine how Shredder works in practice• How effective are the optimizations?• How does it compare with multicores?

• Implementation• Host driver in C++ and GPU in CUDA• GPU: NVidia Tesla C2050 cards• Host machine: Intel Xeon with12 cores

(See paper for details)

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Shredder vs. Multicores

Multicore GPU Basic GPU Async GPU Async + Coalescing

0

0.5

1

1.5

2

2.5

GBp

s 5X

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Outline

• Shredder design• Evaluation• Case studies

• Computation: Incremental MapReduce• Storage: Cloud backup (See paper for details)

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Read input

Map tasks

Reduce tasks

Write output

Incremental MapReduce

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Unstable input partitions

Read input

Map tasks

Reduce tasks

Write output

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GPU accelerated Inc-HDFS

Input file

ShredderHDFS Client

copyFromLocal

Split-1 Split-2 Split-3

Content-based chunking

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Related work

• GPU-accelerated systems• Storage: Gibraltar [ICPP’10], HashGPU [HPDC’10]• SSLShader[NSDI’11], PacketShader[SIGCOMM’10],

…

• Incremental computations• Incoop[SOCC’11], Nectar[OSDI’10],

Percolator[OSDI’10],…

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Conclusions

• GPU-accelerated framework for redundancy elimination• Exploits massively parallel GPUs in a cost-

effective manner

• Shredder design incorporates novel optimizations• More data-intensive than previous usage of GPUs

• Shredder can be seamlessly integrated with storage systems • To accelerate incremental storage and

computation

Thank You!