1. What Happens When Processing Storage Bandwidth are Free and Infinite?
Jim Gray
Microsoft Research

2. Outline Hardware CyberBricks Software CyberBricks What next?
all nodes are very intelligent
Software CyberBricks
standard way to interconnect intelligent nodes
What next?
Processing migrates to where the power is
Disk, network, display controllers have full-blown OS
Send RPCs to them (SQL, Java, HTTP, DCOM, CORBA) to them
Computer is a federated distributed system.

3. A Hypothetical Question Taking things to the limit
Moore’s law 100x per decade:
Exa-instructions per second in 30 years
Exa-bit memory chips
Exa-byte disks
Gilder’s Law of the Telecosom 3x/year more bandwidth ,000x per decade!
40 Gbps per fiber today

4. Grove’s Law Link Bandwidth doubles every 100 years!
Not much has happened to telephones lately
Still twisted pair

5. Gilder’s Telecosom Law: 3x bandwidth/year for 25 more years
Today:
10 Gbps per channel
4 channels per fiber: 40 Gbps
32 fibers/bundle = 1.2 Tbps/bundle
In lab 3 Tbps/fiber (400 x WDM)
In theory 25 Tbps per fiber
1 Tbps = USA 1996 WAN bisection bandwidth
1 fiber = 25 Tbps

6. Thesis Many little beat few big
$1 million
1 MM 3
$100 K
$10 K
Pico Processor
Micro
Nano
1 MB
10 pico-second ram
Mainframe
Mini
10 microsecond ram
10 millisecond disc
10 second tape archive
10 nano-second ram 10 MB 1
0 GB 1 TB 1
00 TB
1.8"
3.5"
2.5"
5.25"
1 M SPEC marks, 1TFLOP
106 clocks to bulk ram
Event-horizon on chip
VM reincarnated
Multi-program cache,
On-Chip SMP
9"
14"
Smoking, hairy golf ball
How to connect the many little parts?
How to program the many little parts?
Fault tolerance?
Gray OGI 12/11/97

7. Billion Instructions/Sec .1 Billion Bytes RAM Billion Bits/s Net
Year B Machine
10 GB byte Disk
.1 B byte RAM
1 Bips Processor
1 B bits/sec LAN/WAN
The Year 2000 commodity PC
Billion Instructions/Sec
.1 Billion Bytes RAM
Billion Bits/s Net
10 B Bytes Disk
Billion Pixel display
3000 x 3000 x 24
1,000 $
Gray OGI 12/11/97

8. 4 B PC’s: The Bricks of Cyberspace
Cost 1,000 $
Come with
OS (NT, POSIX,..)
DBMS
High speed Net
System management
GUI / OOUI
Tools
Compatible with everyone else
CyberBricks
Gray OGI 12/11/97

9. Super Server: 4T Machine
Array of 1,000 4B machines
1 b ips processors
1 B B DRAM
10 B B disks
1 Bbps comm lines
1 TB tape robot
A few megabucks
Challenge:
Manageability
Programmability
Security
Availability
Scaleability
Affordability
As easy as a single system
CPU
50 GB Disc
5 GB RAM
Cyber Brick
a 4B machine
Future servers are CLUSTERS
of processors, discs
Distributed database techniques
make clusters work
Gray OGI 12/11/97

10. Functionally Specialized Cards
P mips processor
Storage
Network
Display
Today:
P=50 mips
M= 2 MB
ASIC
M MB DRAM
In a few years
P= 200 mips
M= 64 MB
ASIC
ASIC

11. It’s Already True of Printers Peripheral = CyberBrick
You buy a printer
You get a
several network interfaces
A Postscript engine
cpu,
memory,
software,
a spooler (soon)
and… a print engine.

12. System On A Chip Integrate Processing with memory on one chip
chip is 75% memory now
1MB cache >> 1960 supercomputers
256 Mb memory chip is 32 MB!
IRAM, CRAM, PIM,… projects abound
Integrate Networking with processing on one chip
system bus is a kind of network
ATM, FiberChannel, Ethernet,.. Logic on chip.
Direct IO (no intermediate bus)
Functionally specialized cards shrink to a chip.

13. All Device Controllers will be Cray 1’s
TODAY
Disk controller is 10 mips risc engine with 2MB DRAM
NIC is similar power
SOON
Will become 100 mips systems with 100 MB DRAM.
They are nodes in a federation (can run Oracle on NT in disk controller).
Advantages
Uniform programming model
Great tools
Security
economics (cyberbricks)
Move computation to data (minimize traffic)
Central
Processor & Memory
Tera Byte
Backplane

14. With Tera Byte Interconnect and Super Computer Adapters
Processing is incidental to
Networking
Storage UI
Disk Controller/NIC is
faster than device
close to device
Can borrow device package & power
So use idle capacity for computation.
Run app in device.
Tera Byte
Backplane

15. Implications Conventional Radical Move app to NIC/device controller
higher-higher level protocols: CORBA / DCOM.
Cluster parallelism is VERY important.
Offload device handling to NIC/HBA
higher level protocols: I2O, NASD, VIA…
SMP and Cluster parallelism is important.
Central
Processor & Memory
Tera Byte
Backplane

16. How Do They Talk to Each Other?
Each node has an OS
Each node has local resources: A federation.
Each node does not completely trust the others.
Nodes use RPC to talk to each other
CORBA? DCOM? IIOP? RMI?
One or all of the above.
Huge leverage in high-level interfaces.
Same old distributed system story.
Applications
Applications
datagrams
streams
RPC ? ?
RPC
streams
datagrams
VIAL/VIPL
VIAL/VIPL
Wire(s)

17. Outline Hardware CyberBricks Software CyberBricks What next?
all nodes are very intelligent
Software CyberBricks
standard way to interconnect intelligent nodes
What next?
Processing migrates to where the power is
Disk, network, display controllers have full-blown OS
Send RPCs to them (SQL, Java, HTTP, DCOM, CORBA) to them
Computer is a federated distributed system.

18. Objects! It’s a zoo ORBs, COM, CORBA,.. Object Relationa1 Databases
Objects and 3-tier computing

19. History and Alphabet Soup
DCE
RPC
GUIDs
IDL
Kerberos
DNS
COM
Microsoft DCOM based on OSF-DCE Technology
DCOM and ActiveX extend it
1985
Solaris
International
UNIX
OSF
DCE
Foundation (OSF)
Open software NT
X/Open
1990
Management
Group (OMG)
Object
CORBA
ODBC
XA / TX
1995
Open
Group
COM
Gray OGI 12/11/97

20. The Promise Both camps Objects are Software CyberBricks
productivity breakthrough (plug ins)
manageability breakthrough (modules)
Microsoft Promises Cairo distributed objects, secure, transparent, fast invocation
IBM/Sun/Oracle/Netscape promise CORBA + Open Doc + Java Beans +
All will deliver
Customers can pick the best one
Both camps
Share key goals:
Encapsulation: hide implementation
Polymorphism: generic ops key to GUI and reuse
Uniform Naming
Discovery: finding a service
Fault handling: transactions
Versioning: allow upgrades
Transparency: local/remote
Security: who has authority
Shrink-wrap: minimal inheritance
Automation: easy

21. The OLE-COM Experience
Macintosh had Publish & Subscribe
PowerPoint needed graphs:
plugged MS Graph in as an component.
Office adopted OLE
one graph program for all of office
Internet arrived
URLs are object references,
Office is Web Enabled right away!
Office97 smaller than Office95 because of shared components
It works!!

22. Linking And Embedding Objects are data modules; transactions are execution modules
Link: pointer to object somewhere else
Think URL in Internet
Embed: bytes are here
Objects may be active; can callback to subscribers

23. Objects Meet Databases basis for universal data servers, access, & integration
Object-oriented (COM oriented) interface to data
Breaks DBMS into components
Anything can be a data source
Optimization/navigation “on top of” other data sources
Makes an RDBMS an O-R DBMS assuming optimizer understands objects
Database
Spreadsheet
Photos
Mail
Map
Document
DBMS
engine

24. The BIG Picture Components and transactions
Software modules are objects
Object Request Broker (a.k.a., Transaction Processing Monitor) connects objects (clients to servers)
Standard interfaces allow software plug-ins
Transaction ties execution of a “job” into an atomic unit: all-or-nothing, durable, isolated
ActiveX Components are a 250M$/year business.
Object Request Broker

25. Object Request Broker (ORB) Orchestrates RPC
Registers Servers
Manages pools of servers
Connects clients to servers
Does Naming, request-level authorization,
Provides transaction coordination
Direct and queued invocation
Old names:
Transaction Processing Monitor,
Web server,
NetWare
Transaction
Object-Request Broker

26. The OO Points So Far Next points: Objects are software Cyber Bricks
Object interconnect standards are emerging
Cyber Bricks become Federated Systems.
Next points:
put processing close to data
do parallel processing.

27. Three Tier Computing Clients do presentation, gather input
Clients do some workflow (Xscript)
Clients send high-level requests to ORB
ORB dispatches work-flows and business objects -- proxies for client, orchestrate flows & queues
Server-side workflow scripts call on distributed business objects to execute task
Presentation
workflow
Business
Objects
Database

28. The Three Tiers DCOM (oleDB, ODBC,...) Object server Pool HTTP+ DCOM
Web Client
HTML
VB or Java
Script Engine
Virt Machine
VBscritpt
JavaScrpt
VB Java
plug-ins
Internet
ORB
HTTP+
DCOM
Object
server
Pool
Middleware
TP Monitor
Web Server...
DCOM (oleDB, ODBC,...)
Object & Data
server.
LU6.2
IBM
Legacy
Gateways

29. Transaction Processing Evolution to Three Tier Intelligence migrated to clients
Mainframe
cards
Mainframe Batch processing (centralized)
Dumb terminals & Remote Job Entry
Intelligent terminals database backends
Workflow Systems Object Request Brokers Application Generators
green
screen
3270
Server
TP Monitor
ORB
Active

30. Web Evolution to Three Tier Intelligence migrated to clients (like TP)
Server
WAIS
Character-mode clients, smart servers
GUI Browsers - Web file servers
GUI Plugins - Web dispatchers - CGI
Smart clients - Web dispatcher (ORB) pools of app servers (ISAPI, Viper) workflow scripts at client & server
archie
ghopher
green screen
Mosaic
NS & IE
Active

31. PC Evolution to Three Tier Intelligence migrated to server
Stand-alone PC (centralized)
PC + File & print server message per I/O
PC + Database server message per SQL statement
PC + App server message per transaction
ActiveX Client, ORB ActiveX server, Xscript
IO request
reply
disk I/O
SQL
Statement
Transaction

32. Why Did Everyone Go To Three-Tier?
Manageability
Business rules must be with data
Middleware operations tools
Performance (scaleability)
Server resources are precious
ORB dispatches requests to server pools
Technology & Physics
Put UI processing near user
Put shared data processing near shared data
Minimizes data moves
Encapsulate / modularity
Presentation
workflow
Business
Objects
Database

33. Why Put Business Objects at Server?
Customer comes to store with list
Gives list to clerk
Clerk gets goods, makes invoice
Customer pays clerk, gets goods
Easy to manage
Clerks controls access
Encapsulation
MOM’s Business Objects
DAD’sRaw Data
Customer comes to store
Takes what he wants
Fills out invoice
Leaves money for goods
Easy to build
No clerks
Gray OGI 12/11/97

34. The OO Points So Far Put processing close to data Next point:
Objects are software Cyber Bricks
Object interconnect standards are emerging
Cyber Bricks become Federated Systems.
Put processing close to data
Next point:
do parallel processing.

35. Parallelism: the OTHER half of Super-Servers
Clusters of machines allow two kinds of parallelism
Many little jobs: Online transaction processing
TPC A, B, C,…
A few big jobs: data search & analysis
TPC D, DSS, OLAP
Both give automatic Parallelism

36. Why Parallel Access To Data?
At 10 MB/s
1.2 days to scan
1,000 x parallel
100 second SCAN.
BANDWIDTH
Parallelism:
divide a big problem
into many smaller ones
to be solved in parallel.
Gray OGI 12/11/97

37. Kinds of Parallel Execution
Any
Any
Sequential
Sequential
Pipeline
Program
Program
Sequential
Partition
outputs split N ways
inputs merge M ways
Sequential
Any
Any
Sequential
Sequential
Sequential
Sequential
Program
Program
Gray OGI 12/11/97

38. Why are Relational Operators Successful for Parallelism?
Relational data model uniform operators
on uniform data stream
Closed under composition
Each operator consumes 1 or 2 input streams
Each stream is a uniform collection of data
Sequential data in and out: Pure dataflow
partitioning some operators (e.g. aggregates, non-equi-join, sort,..)
requires innovation
AUTOMATIC PARALLELISM
Gray OGI 12/11/97

39. Database Systems “Hide” Parallelism
Automate system management via tools
data placement
data organization (indexing)
periodic tasks (dump / recover / reorganize)
Automatic fault tolerance
duplex & failover
transactions
Automatic parallelism
among transactions (locking)
within a transaction (parallel execution)
Gray OGI 12/11/97

40. SQL a Non-Procedural Programming Language
SQL: functional programming language describes answer set.
Optimizer picks best execution plan
Picks data flow web (pipeline),
degree of parallelism (partitioning)
other execution parameters (process placement, memory,...)
Planning
Execution
Monitor
Schema
Executors
GUI
Plan
Optimizer
Rivers
Gray OGI 12/11/97

41. Automatic Data Partitioning
Split a SQL table to subset of nodes & disks
Partition within set:
Range Hash Round Robin
Good for equijoins,
range queries
group-by
Good for equijoins
Good to spread load
Shared disk and memory less sensitive to partitioning,
Shared nothing benefits from "good" partitioning
Gray OGI 12/11/97

42. N x M way Parallelism N inputs, M outputs, no bottlenecks.
Gray OGI 12/11/97

43. Parallel Objects?
How does all this DB parallelism connect to hardware/software Cyber Bricks?
To scale to large client sets
need lots of independent parallel execution.
Comes for from from ORB.
To scale to large data sets
need intra-program parallelism (like parallel DBs)
Requires some invention.

44. Outline Hardware CyberBricks Software CyberBricks What next?
all nodes are very intelligent
Software CyberBricks
standard way to interconnect intelligent nodes
What next?
Processing migrates to where the power is
Disk, network, display controllers have full-blown OS
Send RPCs to them (SQL, Java, HTTP, DCOM, CORBA) to them
Computer is a federated distributed system.
Parallel execution is important

45. MORE SLIDES but there is only so much time.
Too bad

46. The Disk Farm On a Card The 100GB disc card An array of discs
Can be used as
100 discs
1 striped disc
10 Fault Tolerant discs
....etc
LOTS of accesses/second
bandwidth
14"
Life is cheap, its the accessories that cost ya.
Processors are cheap, it’s the peripherals that cost ya
(a 10k$ disc card).

47. Parallelism: Performance is the Goal
Goal is to get 'good' performance.
Trade time for money.
Law 1: parallel system should be
faster than serial system
Law 2: parallel system should give
near-linear scaleup or
near-linear speedup or
both.
Parallel DBMSs obey these laws
Gray OGI 12/11/97

48. Success Stories Online Transaction Processing
many little jobs
SQL systems support
50 k tpm-C (44 cpu, 600 disk 2 node )
Batch (decision support and Utility)
few big jobs, parallelism inside
Scan data at 100 MB/s
Linear Scaleup to 1,000 processors
transactions / sec
hardware
recs/ sec
hardware
Gray OGI 12/11/97

49. The New Law of Computing
Grosch's Law:
Parallel Law:
Needs
Linear Speedup and Linear Scaleup
Not always possible
1 MIPS
1 $
1,000 MIPS
32 $
.03$/MIPS
2x $ is 4x performance
1 MIPS
1 $
1,000 $
1,000 MIPS
2x $ is
2x performance
Gray OGI 12/11/97

50. Clusters being built Teradata 1,000 nodes (30k$/slice)
Tandem,VMScluster 150 nodes (100k$/slice)
Intel, 9,000 55M$ ( 6k$/slice)
Teradata, Tandem, DEC moving to NT+low slice price
IBM: 512 nodes ASCI @ 100m$ (200k$/slice)
PC clusters (bare handed) at dozens of nodes web servers (msn, PointCast,…), DB servers
KEY TECHNOLOGY HERE IS THE APPS.
Apps distribute data
Apps distribute execution
Gray OGI 12/11/97

51. Great Debate: Shared What? SMP or Cluster?
Shared Memory
(SMP)
Shared Disk
Shared Nothing
(network)
Easy to program
Difficult to build
Difficult to scaleup
Hard to program
Easy to build
Easy to scaleup
Sequent, SGI, Sun
VMScluster, Sysplex
Tandem, Teradata, SP2
Winner will be a synthesis of these ideas
Distributed shared memory (DASH, Encore) blurs distinction
between Network and Bus (locality still important)
But gives Shared memory message cost.
Gray OGI 12/11/97

52. BOTH SMP and Cluster? Cluster of PCs Grow Up with SMP
4xP6 is now standard
Grow Out with Cluster
Cluster has inexpensive parts
Cluster
of PCs
Gray OGI 12/11/97

53. Clusters Have Advantages
Clients and Servers made from the same stuff.
Inexpensive:
Built with commodity components
Fault tolerance:
Spare modules mask failures
Modular growth
grow by adding small modules

54. Meta-Message: Technology Ratios Are Important
If everything gets faster & cheaper at the same rate THEN nothing really changes.
Things getting MUCH BETTER:
communication speed & cost 1,000x
processor speed & cost 100x
storage size & cost 100x
Things staying about the same
speed of light (more or less constant)
people (10x more expensive)
storage speed (only 10x better)

55. Storage Ratios Changed
10x better access time
10x more bandwidth
4,000x lower media price
DRAM/DISK 100:1 to 10:10 to 50:1

56. Today’s Storage Hierarchy : Speed & Capacity vs Cost Tradeoffs
Size vs Speed
Price vs Speed 10 15 12 9 6 3 10 4 2 -2 -4
Cache
Nearline
Tape
Offline
Main
Tape
Disc
Secondary
Online
Online
$/MB
Secondary
Tape
Tape
Typical System (bytes)
Disc
Main
Offline
Nearline
Tape
Tape
Cache 10 -9 10 -6 10 -3 10 10 3 10 -9 10 -6 10 -3 10 10 3
Access Time (seconds)
Access Time (seconds)

57. Network Speeds Speed of light did not change
Link bandwidth grew 60% / year
WAN speeds limited by politics
if voice is X$/minute, how much is video?
Gbps to desktop today!
10 Gbps channel is coming.
3Tbps fibers in laboratory thru parallelism (WDM).
Paradox:
WAN link has 40Gbps
Processor bus is Gbps
Comm Speedups
1e 9
1e 8
1e 7
1e 6
1e 5
1e 4
1e 3
Processors (i/s)
LANs & WANs (b/s)
1960
1970
1980
1990
2000
Year
Gray OGI 12/11/97

58. MicroProcessor Speeds Went Up
Clock rates went from 10Khz to 400Mhz
Processors now 6x issue
SPECInt fits in Cache,
it tracks cpu speed
Peak Advertised Performance (PAP) is 1.2 BIPS
Real Application Performance (RAP) is 100 MIPS
Similar curves for
DEC VAX & Alpha
HP/PA
IBM R6000/ PowerPC
MIPS & SGI
SUN
0.1 1 10
100
1000
1980
1990
2000
8088
286
386
486
Pentium P6
Intel MicroProcessor
Speeds (mips)
source: Intel
Gray OGI 12/11/97

59. Performance = Storage Accesses not Instructions Executed
In the “old days” we counted instructions and IO’s
Now we count memory references
Processors wait most of the time
Where the time goes:
clock ticks used by AlphaSort Components
70 MIPS
“real” apps have worse Icache misses so run at 60 MIPS
if well tuned, 20 MIPS if not
Sort
Disc Wait OS
Memory Wait
D-Cache
Miss
I-Cache
B-Cache
Data Miss
Gray OGI 12/11/97

60. Storage Latency: How Far Away is the Data?
Gray OGI 12/11/97

61. Tape Farms for Tertiary Storage Not Mainframe Silos
100 robots
1M$
50TB
50$/GB
3K Maps
10K$ robot
14 tapes
27 hr Scan
500 GB
5 MB/s
20$/GB
Scan in 27 hours.
many independent tape robots
(like a disc farm)
30 Maps

62. The Metrics: Disk and Tape Farms Win
Data Motel:
Data checks in,
but it never checks out
GB/K$ 1 ,
000 ,
000
Kaps
100 ,
000
Maps 10 ,
000
SCANS/Day 1 ,
000
100 10 1
0.1
0.01
1000 x D i
sc Farm
STC Tape Robot
100x DLT
Tape Farm
6,000 tapes, 8 readers

63. Tape & Optical: Beware of the Media Myth
Optical is cheap: 200 $/platter
2 GB/platter
=> 100$/GB (2x cheaper than disc)
Tape is cheap: 30 $/tape
20 GB/tape
=> 1.5 $/GB (100x cheaper than disc).

64. Tape & Optical Reality: Media is 10% of System Cost
Tape needs a robot (10 k$ m$ )
tapes (at 20GB each) => 20$/GB $/GB
(1x…10x cheaper than disc)
Optical needs a robot (100 k$ )
100 platters = 200GB ( TODAY ) => 400 $/GB
( more expensive than mag disc )
Robots have poor access times
Not good for Library of Congress (25TB)
Data motel: data checks in but it never checks out!

65. The Access Time Myth The Myth: seek or pick time dominates
The reality: (1) Queuing dominates
(2) Transfer dominates BLOBs
(3) Disk seeks often short
Implication: many cheap servers better than one fast expensive server
shorter queues
parallel transfer
lower cost/access and cost/byte
This is now obvious for disk arrays
This will be obvious for tape arrays

66. Billions Of Clients Every device will be “intelligent”
Doors, rooms, cars…
Computing will be ubiquitous
Gray OGI 12/11/97

67. Billions Of Clients Need Millions Of Servers
All clients networked to servers
May be nomadic or on-demand
Fast clients want faster servers
Servers provide
Shared Data
Control
Coordination
Communication
Clients
Mobile clients
Fixed clients
Servers
Server
Super
server
Gray OGI 12/11/97

68. 1987: 256 tps Benchmark 14 M$ computer (Tandem) A dozen people
False floor, 2 rooms of machines
Admin expert
Hardware experts
A 32 node processor array
Auditor
Network expert
Simulate 25,600 clients
Manager
Performance expert
OS expert
DB expert
A 40 GB disk array (80 drives)

69. 1988: DB2 + CICS Mainframe 65 tps
IBM 4391
Simulated network of 800 clients
2m$ computer
Staff of 6 to do benchmark
2 x 3725
network controllers
Refrigerator-sized
CPU
16 GB disk farm
4 x 8 x .5GB

70. 1997: 10 years later 1 Person and 1 box = 1250 tps
1 Breadbox ~ 5x 1987 machine room
23 GB is hand-held
One person does all the work
Cost/tps is 1,000x less 25 micro dollars per transaction
4x200 Mhz cpu
1/2 GB DRAM
12 x 4GB disk
Hardware expert
OS expert
Net expert
DB expert
App expert
3 x7 x 4GB
disk arrays

71. What Happened?
Moore’s law: Things get 4x better every 3 years (applies to computers, storage, and networks)
New Economics: Commodity class price/mips software $/mips k$/year mainframe , minicomputer microcomputer
GUI: Human - computer tradeoff optimize for people, not computers
mainframe
mini
micro
time
price

72. What Happens Next ? Last 10 years: 1000x improvement
1985
2005
1995
performance ?
Last 10 years: x improvement
Next 10 years: ????
Today: text and image servers are free 25 m$/hit => advertising pays for them
Future: video, audio, … servers are free “You ain’t seen nothing yet!”

73. Smart Cards Then (1979) Now (1997)
EMV card with dynamic authentication
(EMV=Europay, MasterCard, Visa standard)
door key, vending machines, photocopiers
Now (1997)
Bull CP8 two chip card
first public demonstration 1979
Courtesy of Dennis Roberson NCR.

74. Applications Memory Capacity 16 KB today but growing
Smart Card
Memory Capacity
Source: PIN/Card -Tech/ Courtesy of Dennis Roberson NCR
1990
1992
1996
1998
2000
2002
Memory Size (Bits)
300 M
1 M
3 K
10 K
You are here
2004
16 KB today
but growing
super-exponentially
Applications
Cards will be able to store
data (e.g. medical)
books, movies,…
money
One of the factors limiting smart card deployment is the limited memory size that can be stored on the card. Smart cards today with 3 to 10 kilobytes of storage have advantages over magnetic stripe cards, but are limited in their ability to carry massive amounts of application data.
As memory costs continue to improve, and miniaturization of the chips continues to improve, smart cards will move to a few hundred megabytes thus having the ability to store sufficient amounts of data to perform practical applications.
The past tens years of smart card evolution has taught us that no single application is significantly strong to drive the market acceptance. Multifunction cards with massive memory capabilities can and will change that.
Gray OGI 12/11/97 20 5 7