Shimin Chen Big Data Reading Group

1. Gordon: Using Flash Memory to Build Fast, Power-efficient Clusters for Data-intensive ApplicationsA. Caulfield, L. Grupp, S. Swanson, UCSD, ASPLOS’09 Shimin Chen Big Data Reading Group

2. Introduction • Gordon: a flash-based system architecture for massively parallel, data-centric computing. • Solid-state disks • Low-power processors • Data-centric programming paradigms • Can deliver: • Up to 2.5X the computation per energy of a conventional cluster based system • Increasing performance by up to 1.5X

3. Outline • Gordon’s system architecture • Gordon’s storage system • Configuring Gordon • Discussion • Summary

4. Gordon’s System Architecture Gordon node 16 nodes in an enclosure

5. Prototype Photo: ASPLOS’09 paper uses simulation http://www-cse.ucsd.edu/users/swanson/projects/gordon.html

6. Gordon node • Configuration: • 256GB of flash storage • A flash storage controller (w/ 512MB dedicated DRAM) • 2GB ECC DDR2 SDRAM • 1.9Ghz Intel Atom processor • Running a minimal linux installation • Power: no more than 19w • Compared to 81w of a server • 900MB/s read and write bandwidth to 256GB disk

7. Enclosures • Within an enclosure, 16 nodes plug into a backplane that provides 1Gb Ethernet-style network • A rack holds 16 enclosures (16x16=256 nodes) • 64 TB of storage • 230 GB/s of aggregate IO bandwidth

8. Programming • From the SW and users’ perspectives, a Gordon system appears to be a conventional computing cluster • Benchmarks: Hadoop

9. Outline • Gordon’s system architecture • Gordon’s storage system • Configuring Gordon • Discussion • Summary

10. Flash memory Flash memory Flash memory Flash memory Flash Array Hardware The flash controller has over 300 pins 2GB System DRAM CPU 512MB FTL DRAM Flash controller 4 buses, each support 4 flash packages

12. Super-Page • Three ways to stripe data across flash memory • Horizontal: across buses • Vertical: across packages on the same bus • 2-D: combined

13. Bypassing and Write Combining • Read bypassing: merging read requests to the same page • Write combining: merging write requests to the same page

14. Trace-Based Study 64KB super-page achieves the best performance

15. Outline • Gordon’s system architecture • Gordon’s storage system • Configuring Gordon • Discussion • Summary

16. Workloads • Map-Reduce workloads using Hadoop Each workload is run on 8 2.4GHz Core 2 Quad machines with 8GB RAM and a single SATA hard disk with 1Gb Ethernet to collect traces.

17. Power Model

18. Simulation • High-level trace-driven cluster simulator • Trace: second-by-second utilization (number of instructions, number of L2 cache misses, number of bytes sent and received over network, number of hard drive accesses) • Replay the trace for a conventional cluster model and a Gordon model • Detailed storage system simulators • Disksim for simulation 1TB, 7200rpm SATAII hard drive • Gordon simulator

20. 88 different node configurations

21. Optimal Designs • MinT: Min Time • MaxE: Max performance/watt • MinP: Min Power

23. Outline • Gordon’s system architecture • Gordon’s storage system • Configuring Gordon • Discussion • Summary

24. Exploit Disks • Cheap redundancy • Virtualize Gorgon: • Gordon + a large disk-based data warehouse • Load data from disk storage into Gordon for a job • May overlap data transfer time for preparing a job with computation

25. System Costs Actual price is $7200

26. System Costs for Storing 1PB Data

27. Summary • Use flash memory + low-power processor (Atom) • Support data intensive computing: such as Map-Reduce operations • The design choice is attractive because of higher power/performance efficiency