1. The SC Grid Computing Initiative Kirk W. Cameron, Ph.D. Assistant Professor Department of Computer Science and Engineering University of South Carolina

2. The Power Grid Reliable Universally accessible Standardized Low-cost Billions of different devices Resource Transmission Consumption on Demand Distribution

3. The Computing Grid Reliable Universally accessible Standardized Low-cost Billions of different devices Resource Consumption on Demand Distribution Transmission

4. What is a Grid? H/W + S/W infrastructure to provide access to computing resources † –Dependable (guaranteed performance) –Consistent (standard interfaces) –Pervasive (available everywhere) –Inexpensive (on-demand to decrease overhead) † The Grid: Blueprint for a Future Computing Infrastructure, I. Foster and C. Kesselman (Eds), Morgan-Kaufmann Publishers, 1998.

5. Examples Problem: Need to project companies computing needs –Solution: Computing on Demand –Example: Any company with medium-large # employees Problem: Computational needs exceed local abilities (cost) –Solution: Supercomputing on Demand –Example: Aerodynamic simulation of vehicles Problem: Data sets too large to be held locally –Solution: Collaborative computing with regional centers –Example: DNA sequencing analysis --> derive single sequence locally, compare to large database that is non-local Private Sector Interest –Grid engines (SW), Hardware support, Outsourcing –IBM (PC Outsourcing), Sun (Grid One Engine)

6. Large-scale Example of a Grid † The TeraGrid project is funded by the National Science Foundation and includes five partners: NCSA, SDSC, Argonne, CACR and PSC NSF TeraGrid

7. Small-scale Example SC Grid Initiative

8. Electric Power Supply Circa 1910 –Electric Power can be generated –Devices are being designed to use electricity –Users lack ability to build and operate their own generators Electric Power Grid –reliable, universally-accessible, standardized, low-cost, transmission and distribution technologies –Result: new devices and new industries to manufacture them Circa 2002 –Billions of devices running on reliable, low-cost power

9. My background I’m a “systems” person –Intel Corporation SMP memory simulation –Los Alamos National Laboratory Performance Analysis Team (Scientific Computing) DOE ASCI Project (TERA- and PETA-scale scientific apps) –2 nd year at USC Courses: Comp Arch, Parallel Comp Arch, Perform Analysis Research: Parallel and Distributed Computing, Computer Architecture, Performance Analysis and Prediction, Scientific Computing Interests: Identifying and improving performance of scientific applications through changes to algorithm and systems design (hardware, compilers, middleware (OS+))

10. Outline Gentle intro to Grid SC Grid Computing Initiative Preliminary Results SCAPE Laboratory

11. Computational Power Supply Analogous to Power “Grid” –Heterogeneity (generators/outlets vs. machines/networks) –Consumer requirements Power consumption vs. computational requirements Service guarantees vs. QOS Money to be made vs. money to be made –Economies of scale (power on demand?) –Political influence at large scale Local control necessary, with interfaces to outside (standards)

12. Why now? Technological improvements (cpu, networks, memory capacity) Need for demand-driven access to computational power (e.g. MRI) Utilization of idle capacity (cycle stealing) Sharing of collaborative results (virtual laboratories over WANs) Utilize new techniques and tools –Network enabled solvers (Dongarra’s NetSolve at UT-Knoxsville) –Teleimmersion (collaborative use of Virtual Reality: Argonne, Berkeley, MIT)

13. Applications of Grid Computing Distributed Supercomputing –Maximize available resources for large problems –Large-scale scientific computing –Challenges Scalability of service Latency tolerance Heterogeneous system high performance On-demand Computing –Access to non-local resources – computation, s/w, data, sensors –Driven by cost/performance over absolute performance –Example: MUSC MRI data analysis

14. Applications of Grid Computing High-Throughput Computing –Scheduling large number of independent tasks –Condor (Wisconsin) –http://setiathome.ssl.berkeley.edu/http://setiathome.ssl.berkeley.edu/ Data Intensive Computing –Data analysis applications –Grid Physics Network (http://www.griphyn.org/)http://www.griphyn.org/ Collaborative Computing –Virtual shared-space laboratories –Example: Boilermaker (Argonne) Collaborative, interactive design of injective pollution control systems for boilers (http://www.mcs.anl.gov/metaneos)http://www.mcs.anl.gov/metaneos

15. Other Grid-related Work General Scientific Community (http://www.gridforum.com) –NSF Middleware Initiative –Globus Project –Condor Project –Cactus Project –See grid forum for long list…

16. Outline Gentle intro to Grid SC Grid Computing Initiative Preliminary Results SCAPE Laboratory

17. SC Grid Initiative Immediate increase in local computational abilities Ability to observe application performance and look “under the hood” Interface infrastructure to link with other computational Grids Ability to provide computational power to others on-demand “Tinker-time” to establish local expertise in Grid computing Incentive to collaborate outside university and obtain external funding

18. SC Grid Milestones Increase computational abilities of SC –Establish prelim working Grid on SC campus –Benchmark prelim system configurations –Use established testbed for Scientific computing Multi-mode computing Middleware Development –Establish dedicated Grid resources $45K Equipment Grant from USC VP Research Extend SC Grid boundaries –Beyond department resources –Beyond campus resources (MUSC) –Beyond state resources (IIT) Incorporate other technologies –Multi-mode applications (e.g. Odo) Grid-enabled as of 1 Nov 02! In progress done

19. Outline Gentle intro to Grid SC Grid Computing Initiative Preliminary Results Synergistic Activities

20. USC CSCE Department Mini Grid

21. Node Configurations Resources#HWOSNetworking Beowulf (for research) 1 1 master node + 32 slave nodes PIII 933M, 1GB Mem RedHat Linux 7.110/100M NIC SUN Ultra10 (for teaching) 17UltraSPARC-Iii 440M, 256MB memory Solaris 2.9 10/100M NIC SUN Blade100 (for teaching) 21 UltraSPARC-IIe 502M, 256MB memory Solaris 2.9 10/100M NIC SUN Blade150 (for teaching) 2 UltraSPARC-IIe 650M, 256MB memory Solaris 2.9 10/100M NIC

22. Benchmark and Testing Environment NPB (NAS Parallel Benchmarks 2.0) –Specifies a set of programs as benchmarks –Each benchmark has 3 problem sizes Class A: for moderately powerful workstation Class B: high-end workstations or small parallel systems Class C: high-end supercomputing We tested the performance of –EP kernel is "embarrassingly" parallel in that no communication is required for the generation of the random numbers itself. –SP kernel solves three sets of uncoupled systems of equations, first in the x, then in the y, and finally in the z direction. These systems are scalar penta-diagonal. Running Setting –When #node <= 16, EP is run on NOWs –When #node > 16, EP is run on NOWs and Beowulf

23. Execution time for EP (Class C) Performance doubles with number of nodes. Except here

24. MFLOPS for EP (Class C) MFLOPS overall shows same trend. (Performance doubles with number of nodes.) Except here

25. Node Performance for EP (Class C) MFLOPS/node illustrates reason for less than optimal application scalability. At this point we incorporate older Sun machines (SunBlade  SunUltra)

26. Execution Time for SP (Class B) More realistic problems will have performance bottlenecks: need analysis to run efficiently

27. Kirk W. Cameron, Ph.D. Assistant Professor Department of Computer Science and Engineering University of South Carolina