Scalability of Problem Solving Environments
and Wrap Up Session
Ahmed Sameh Minnesota
Wayne Joubert LANL
Barry Smith ANL
John Rice Purdue
Sameh Presentation
*	Scalable Algorithms for sparse problems adjust algorithms -- see METIS by Kumar for partitioning and matrix reordering
*	SPARSLIB
-	Iterative solvers not as robust as he would like
-	Can tailor to user’s data structures
Barry Smith Presentation
*	Levels of abstraction
-	Application Interface
-	Math Interface
*	Data encapsulation(=hidden)
*	Use C as an example to remove compiler manipulation
-	objects carry with them routines to manipulate objects
-	in C specify functions by pointers to routines
*	State Encapsulation
-	Could use a global array -- common block but this is not good if recursive
-	So use a user supplied data structure
John Rice Presentation
*	Parallelism can be exploited at all levels from user to utilities
*	Data Parallelism in space is key
-	need to allow variable in space algorithms
*	Collatz conjecture (1970ish) -- time to solve PDE of same order as time to evaluate closed form solution
Joubert Presentation -- I
*	Amoco Cray LANL collaboration to develop parallel oil reservoir simulation system
-	GUI, visualization, geostatics,history matching. simulation itself
-	Want 100 times a Cray2
-	Must run overnight
*	Need scalability -- PC to MPP
Joubert Presentation -- II
*	Port to 128 node T3D -- initially 7-8 mflops per node
-	drastic optimization gives 18 mflops/node with 1 to 2 man-months
*	2200 wells, 5 years, 5 million grid points
-	run 50 such cases by end of year
*	Hindrances
-	lack of convergence of architectures and their short lifetime
-	no clear standards for sparse linear algebra
-	would like standard sparse BLAS so vendor would automatically optimize
Questions from Audience -- I
*	Physics of compositional models makes load balancing critical not solver
-	however Amoco problem is not of this type
*	Smith with usual glee with pointers(!) says we will use unstructured meshes for everything to allow natural load balancing
*	General discussion of load balancing ensues but little/new deep thoughts
Questions from Audience -- II
*	Memory used could be a serious issue on the web as can’t predict how much memory availabe
-	somebody claims (remarkably) that cache based machines run irregular meshs as well as regular meshs
*	Skjellum notes that pointers in C++ are class relative so can move from machine to machine
*	Incorrect discussion of multithreaded models for load balancing irregular problems
Overall Conclusions -- I
*	Hardware will change
*	Need to support multiple paradigms
*	PSE’s will deliver scalable libraries
*	Need Examples of PSE’s inside and outside PDE domain
-	Purdue had a table of some 20 PDE PSE’s
Overall Conclusions -- II
*	PSE’s are good but need study of software architecture and tools to build them
*	PSE’s will build on PSE’s
*	Can re-use PSE modules and PSE infrastructure
*	Standards for Sparse libraries