1. Berkeley Cluster: Zoom Project
3 TB storage system
370 8 GB disks, MHz PPro PCs, 100Mbit Switched Ethernet
System cost small delta (~30%) over raw disk cost
Application: San Francisco Fine Arts Museum Server
70,000 art images online
Zoom in 32X; try it yourself!
(statue)
Fine Arts Project
- High-resolution pictures of over 60,000 objects of art stored on PhotoCD
- Database allows users to search the images using keywords (titles, artist’s names)
- Images converted to tiled format (GridPix), to ease zooming and scrolling within an image

2. User Decision Support Demand vs. Processor speed
Database demand:
2X / 9-12 months
Database-Proc.
Performance Gap:
“Greg’s Law”
Moore’s Law is a laggard
250%/year for Greg
60%/year for Moore
7%/year for DRAM
Decision support is linear in database size
CPU speed
2X / 18 months
“Moore’s Law”

3. Outline Technology: Disk, Network, Memory, Processor, Systems
Description/Performance Models
History/State of the Art/ Trends
Limits/Innovations
Technology leading to a New Database Opportunity?
Common Themes across 5 Technologies
Hardware & Software Alternative to Today
Benchmarks

4. Review technology trends to help?
Desktop Processor:
+ SPEC performance
– TPC-C performance, – CPU-Memory perf. gap
Embedded Processor: + Cost/Perf, + inside disk – controllers everywhere
Disk Memory Network
Capacity …
Bandwidth + + +
Latency – – –
Interface – – –

5. IRAM: “Intelligent RAM”C C
Proc
B u s
I/O
I/O
Microprocessor & DRAM on a single chip:
on-chip memory latency 5-10X, bandwidth X
serial I/O 5-10X v. buses
improve energy efficiency 2X-4X (no off-chip bus)
reduce number of controllers
smaller board area/volume $ $
L2$ C C
Bus
Bus C
$B for separate lines for logic and memory
Single chip: either processor in DRAM or memory in logic fab D R A M
I/O
I/O
...
Proc D R A M
Bus D R A M

6. “Intelligent Disk”(IDISK): Scalable Decision Support?
Low cost, low power processor & memory included in disk at little extra cost (e.g., Seagate optional track buffer)
Scaleable processing AND communication as increase disks
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How does TPC-D scale with dataset size?
Compare NCR 5100M 20 node system (each node is MHz Pentium CPUs),
March 28, 1997; 100 GB, 300GB, 1000GB
Per 19 queries, all but 2 go up linearly with database size: (3-5 vs 300, 7-15 vs. 1000)
e.g, interval time ratios 300/100 = 3.35; 1000/100=9.98; 1000/300= 2.97
How much memory for IBM SP2 node?
100 GB: 12 processors with 24 GB; 300 GB: 128 thin nodes with 32 GB total; 256 MB/node (2 boards/processor)
TPC-D is business analysis vs. business operation
17 read only queries; results in queries per Gigabyte Hour
Scale Factor (SF) multiplies each portion of the data: 10 to 10000
SF 10 is about 10 GB; indices + temp table increase 3X - 5X
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cross bar
IRAM
IRAM
IRAM
IRAM … … … … … …
IRAM
IRAM
IRAM
IRAM … … …