1. The Arabica Project A distributed scientific computing project based on a cluster computer and Java technologies.
Daniel D. Warner
Dept. of Mathematical Sciences
Clemson University

2. Historical Trends Complex simulations are one of a kind
Supported by a team of experts
Special hardware
Vector processors,, Hypercubes, Clusters
CPU speeds have increased faster than communication speeds
Sophistication in software packaging
libraries, objects, components, frameworks

3. The Arabica Goals Researcher Efficiency Programmer Productivity
Help scientists do science.
Programmer Productivity
Dramatically improve programming.
Hardware Performance
Cluster computing.
Leverage the 10% of the code that uses 90% of the time.

4. Researcher Efficiency
Researcher Team
Simultaneous access over the Internet.
Simultaneous and modifiable views of the same running simulation.
Chat support.
Other Portal Properties
List of services with web documentation.
Authorization for each service.

5. Programmer Productivity
Use Java Technologies to build distributed applications.
Use Java wrappers to utilize existing application code, particularly well-tested Fortran and C libraries
LAPack and BLAMs
Exploit code migration to minimize code administration

6. Hardware Performance
Use distributed computing at the level of coarse grained parallelism
Use low level code (C, assembly, or optimized Fortran) to achieve optimal on-chip performance of key kernels.
Use JNI, Java’s Native Interface to tie low level code into distributed objects.

7. Quick Tour of the Arabica cluster
Robert Simms, the grad student who got the cluster up and running, working with Henrici, the master computer. Henrici is a 500 MHz PowerMac G4 with 256 MB of ram and 15 GB of hard drive. Henrici is running Mac OS X with Apple’s Java 1.2

8. Quick Tour of the Arabica cluster
The first cabinet contains six 66 MHz PowerMac 6100s with 16 MB of ram and 500 MB of hard drive. Each machine is running the MkLinux operating system with Blackdown Java 1.3.

9. Quick Tour of the Arabica cluster
These slave machines are awaiting power and ethernet cables.
Both Mac OS X and MkLinux are based on Carnegie Mellon’s Mach kernel, which supports lightweight threads. The JVMs on all systems are running with native threads.

10. Quick Tour of the Arabica cluster
These slave machines are awaiting power and ethernet cables.

11. Java Technologies RMI - RPC with a difference JINI JavaSpaces
Code migration, only need interfaces
String based lookup service
JINI
Object and attribute lookup service
Transaction service.
JavaSpaces
Linda style system

12. Java Technologies - RMI
Let’s computing be done on the appropriate machine
Can start with thin clients
Can start with thin servers
Program to the interface
Remote and Migrateable are simple design decisions

13. Java Technologies - JINI
Flexibility
Register a new service and its available
Reliability
Leases and Transactions allow for communication failures and crashes

14. Java Technologies - JavaSpaces
Based on Linda
David Gerlenter introduced “tuple” spaces in 1982
Decouples sender and receiver
Partial matching
Adds Objects
Stores serialized objects with attributes
Matches these against “template” objects

15. Examples HDF Viewer Fraqctal Microsope GalaxSee Clique Tree Solver
Graphical and spreadsheet views of data
Fraqctal Microsope
Interactive Mandelbrot set
GalaxSee
Interactive “galaxy” simulation
Clique Tree Solver
Solves sparse linear systems of equations

16. HDF Viewer NCSA’s Hierarchical Data Format Model, View, Controller
Preliminary development, Spring 2000.
Starts with selection of data files.
Model, View, Controller
Multiple views of the same data set.
Can edit data in one view and see it reflected in all the other views.
Model is a remote object.

17. Fractal Microscope Client Manager Slaves Supplies region
Draws the data selects the color scheme
Manager
Drops tasks into the JavaSpace.
Assembles results and sends to client.
Slaves
Grab tasks and compute the results.

18. GalaxSee Client Manager Slaves
Specifies number and properties of stars
Interactive 3D display of stars
Manager
Drops tasks into the JavaSpace.
Assembles results and sends to client.
Slaves
Grab tasks and compute results.

19. Clique Tree Solver Manager Slaves Finds the clique-tree ordering
Determines submatrices
Manages the hierarchical assembly
Slaves
Factor submatrices.

20. Deliverables Open source project Technical notes Courses Frameworks
MTHSC 983, SS II 2001
MTHSC 983, Spring 2002
Frameworks
Application Examples