1. (High-End) Computing Systems Group Department of Computer Science and Engineering The Ohio State University

2. Computation spread over hundreds and thousands of processors Provides far-reaching benefits to society: –new drugs, –safer and fuel-efficient vehicles, –environmental modeling – weather and climate prediction –scientific discoveries in a broad range of disciplines Essential to commercial domains as well –Web servers, large databases, search engines High-End Computing and Its Benefits

3. Cover the entire range, from the Applications level, through the Systems Software level, to the Networking and Communications level, in developing high-end systems and enabling their use in application areas: –Data-Intensive Computing –Data-mining and Database Systems –High-Performance Scientific Computing –Middleware and Compilers –Network-Based Computing Carry out research in an integrated manner (systems, networking, and applications) Our Vision

4. The cost/performance ratio of computing, networking, and storage components has improved dramatically, making the aggregate available “raw” computing power very high However, the fraction of the potential power that is actually utilized is getting smaller every year Two broad challenges: –Make the large aggregate power of distributed computing resources available in a transparent manner to high-end applications –Create high-level approaches to ease the development of high-end applications The Research Challenges

5. Par/Dis Applications Systems Software Networking & Storage Middleware & Compilers Scheduling, Dependability And Security Bio-Informatics Databases and Datamining Scientific Computing Agrawal, Qin, Sadayappan Saltz, Ferhatosmanoglu Parthasarathy Communication Protocols Active Network Interfaces, Data Access Research Areas Covered Panda, Lauria Zhang

6. Gagan Agrawal –Grid Computing, Middleware, Data Mining, Biological Data Integration Hakan Ferhatosmanoglu –Databases Mario Lauria –I/O and Communication, Computational Biology D. K. Panda –Architecture, Communication, & Networking Srini Parthasarathy –Data-intensive computing & Data Mining Feng Qin –Operating Systems, Security and Dependability P. Sadayappan –Performance Optimization, Compilers, & Scheduling Joel Saltz –High Performance Computing Software & Bioinformatics Xiaodong Zhang –Distributed Systems, Memory Systems (also Networking) Faculty Involved

7. Faculty, Students, Funding, and Accomplishments Over 60 graduate students involved in research in the Systems area –More than 45 funded as RAs Two post-docs Research expenditure for FY ’05 ~ $2.5M Several large-scale grants –Four NSF medium-sized ITR (Saday, Joel(2), Srini) –DOE SciDAC (DK (2)) –NSF RI Award (DK and the group) –COE:P award (Agrawal and others) Several CAREER Award Winners –Srini, Hakan (NSF and DOE) –Gagan (NSF) –DK (NSF)

8. Six Best Paper Awards in major conferences during the last four years First employment of graduated students –Arizona State University, Louisiana State University, IBM TJ Watson, IBM Research, Argonne National Lab, SGI, Compaq/Tandem, Pacific Northwest National Lab, Fore Systems, Microsoft, Lucent, Citrix, Dell, Yahoo, Amazon, Oracle, Ask.com, …. Several of the past and current students –OSU Graduate Fellowship –OSU Presidential Fellowship –IBM Co-operative Fellowship –CSE annual research award Faculty, Students, Funding, and Accomplishments

9. Investigators are strong players in many National- level Initiatives –NPACI –DOE Programming Models –HUBS/DARPA Collaborations with major industry and National Labs Open-source Developments and Distribution –Datacutter; also part of NPACkage –MVAPICH (MPI over InfiniBand) more than 400 organizations world-wide –TCE (Tensor Contraction Engine) for Computational Chemistry Participation in National-Level Initiatives and Open-Source Developments

10. Projects: Gagan Agrawal Overall Agenda: Efficient Processing of Data arising from distributed sources Research Components:  Middleware for Streaming Data (GATES)  Investigate self-adaptation, process migration, fault-tolerance..  Middleware for Data Analysis in Clusters and Grids (FREERIDE and FREERIDE-G)  Investigate Parallelization from high-level API, Remote Data Access  Automatic Data Virtualization and Wrapper Generation Frameworks  Management of large scale data, especially from Sensors and Scientific Experiments  Biological Data Integration  Others: Parallel Compilation, Query Optimization (XQuery), Data Mining and OLAP algorithms

11. Overall: Data Management in Modern Applications –Problem: Massive amount of Multi-dimensional data –Goal: Scalable systems for Efficient queries Multimedia Data Management Image, audio, video, document databases (DBs) Spatial DBs: GIS Time-series DBs: stock market Bioinformatics & Biomedical Data Management –Genome data: DNA, proteins, gene expression data –Structural analysis of bio-chemical data Stream and Sensor Data Management –Telecommunications and internet data management –sensor networks: geo-sensors, bio-sensors Parallel I/O Projects: Hakan Ferhatosmanoglu

12. Projects: D. K. Panda System Software/Middleware –High Performance MPI on InfiniBand Cluster –Clustered Storage and Parallel File Systems –Solaris NFS over RDMA –iWARP and its Benefits to High Performance Computing –Efficient Shared Memory on High-Speed Interconnects –High Performance Computing with Virtual Machines (Xen-IB) –Design of Scalable Data-Centers with InfiniBand Networking and Communication Support –High Performance Networking for TCP-based Applications –NIC-level Support for Collective Communication and Synchronization –NIC-level Support for Quality of Service (QoS) –Micro-Benchmarks and Performance Comparison of High-Speed Interconnects More details on http://nowlab.cse.ohio-state.edu/  Projectshttp://nowlab.cse.ohio-state.edu/

13. Projects: Feng Qin Agenda: Dependability and Security of Computer Systems Online Techniques for System Dependability and Security –Failure recovery for high-end parallel and distributed systems –Online bug diagnosis, i.e., identifying root causes of software bugs –Dynamic system security enhancement based on runtime information Software Debugging –Bug detection (esp. concurrency bugs) in multi-core, parallel and distributed systems –Automated software bugs localization/isolation (e.g. across different versions)

14. Projects: P. (Saday) Sadayappan Systems Support for High-Level Parallel Programming: Goal is to enable higher level programming than message passing (MPI), without sacrificing performance –Automatic synthesis of high-performance parallel programs for a class of quantum chemistry computations –Compiler optimizations for locality enhancement and communication minimization. –Parallel Global-Address-Space programming with MATLAB –Scheduling and load balancing –Performance Optimization for multi-core architectures –CIS 888.11K (every quarter)

15. Agenda: Data Mining and Parallel/Distributed Systems. Systems Support for Data Mining Applications –Resource and Location Aware Data Management and Mining for Dynamic (potentially streaming) & Distributed Datasets. –Distributed Shared State for Interactive Applications Fundamental Algorithms and Techniques –Incremental Techniques for Mining Streaming Datasets –Parallel and Distributed Data Mining Algorithms Applications Research –Intrusion Detection –Scientific & Biomedical Data Mining –Web/Text Mining Projects: Srinivasan Parthasarathy

16. Dynamic Data Driven Applications Systems –Data-intensive and Grid Computing tools and frameworks targeting data/compute intensive applications –Runtime and compiler support –Component frameworks for combined task and data parallelism in heterogeneous environments –Service-oriented Architectures for Grid-enabled data-intensive computing Scheduling Services in the Grid Grid Generalized Reduction Active Semantic Data Caching Data Cluster/Decluster/Range Query Services Application Areas include –Earth Systems Sciences: Instrumented oil field simulations, seismic data analysis. –Medical Imaging: Texture analysis, segmentation, registration of ensembles of multi-modal, multi-resolution, time-dependent imagery. –Pathology Informatics: Visualization and exploration of digitized pathology slides –Bioinformatics: Querying and analysis of large databases of gene and protein sequence data. –Medical Informatics: Ad-hoc, federated data warehouses. Projects: Joel Saltz (Bioinformatics and CSE)

17.  Putting Disk Layout Information on the OS map  building a Disk-Seen system to exploit Dual LOcality (DULO): temporal locality and disk spatial locality.  DULO-caching and DULO-prefetching.  Disk Energy Saving  caching and prefetching in flash drive.  Multi-level disk caching and prefetching.  Cooperative I/O buffer caching in large clusters.  DNS caching consistency Xiaodong Zhang: Data Access in Core and Distributed Systems

18. Wyckoff: High-performance Storage Investigate: –Object-based storage devices used in parallel file systems Goal: –Enable greater scalability and higher performance of large storage systems by interacting with disks at a higher semantic level. NSF-funded project starting Sep 06 for 3 years, looking for student(s) Involves aspects of systems, storage, protocols and networking Helpful skills: –C programming –Unix/Linux systems –Parallel computing –File systems Contact: pw@osc.edu

19. High-End Computing and Networking Research Testbed for Next Generation Data Driven, Interactive Applications PIs: D. K. Panda, G. Agrawal, P. Sadayappan, J. Saltz and H.-W. Shen Other Investigators: S. Ahalt, U. Catalyurek, H. Ferhatosmanoglu, H.-W. Jin, T. Kurc, M. Lauria, D. Lee, R. Machiraju, S. Parthasarathy, P. Sinha, D. Stredney, A. E. Stutz, and P. Wyckoff Dept. of Computer Science and Engineering, Dept. of Biomedical Informatics, and Ohio Supercomputer Center The Ohio State University Funded by NSF Research Infrastructure (RI) Program Award: $3.1M = $1.53M (from NSF) + $1.48M (Cost-Sharing from OBR and OSU) Contact: panda@cse.ohio-state.edu

20. Our Vision of the Next Generation Architecture Client 1 Client 2 Client N Computing Environment - Basic Processing - Post-processing Data Repository 10-100 TeraBytes - Pre-processing Wide Area Network (WAN) Wired or Wireless Network Memory Cluster LAN/SAN Interactive Collaborative End-to-end QoS

21. Experimental Testbed Installed CSE 70-node Memory Cluster with 512 GBytes memory, 24 TB disk GigE, and InfiniBand SDR Upgrade (Yr4) BMI Mass Storage System 500 TBytes (Existing) OSC 64-node Compute Cluster with InfiniBand DDR and 4TB disk 10 GigE on some (to be added) Upgrade (Yr4) Graphics Adapters and Haptic Devices 20 Wireless Clients & 3 Access Points Upgrade (Yr4) Video wall 2x10=20 GigE 40 GigE (Yr4) 2x10=20 GigE 40 GigE (Yr4) 10.0 GigE switch 10.0 GigE switch 10.0 GigE switch

22. Collaboration among the Components and Investigators Networking, Communication, QoS, and I/O Programming Systems and Scheduling Data Intensive Algorithms Data Intensive Applications Panda, Jin, Lee, Lauria, Sinha, Wyckoff, and Kurc Saltz, Agrawal, Sadayappan, Kurc, Catalyurek, Ahalt, and Hakan Shen, Agrawal, Machiraju, and Parthasarathy, Saltz, Stredney, Sadayappan Machiraju, Parthasarathy, Catalyurek, and Other OSU collaborators

23. Wright Center for Innovation (WCI) A new funding to install a larger cluster with 64 nodes with dual dual-core processors (up to 256 processors) Storage nodes with 40 TBytes of space Connected with InfiniBand DDR Focuses on Advanced Data Management

24. High Perf. Comp. Architecture Operating Systems Databases/DataMining Languages/Compilers CSE 621 CSE 775 CSE 760 CSE 671 CSE755 CSE 721 CSE875 CSE 762 CSE772 CSE756 621, 756 are only offered in Autumn 721 is only offered in Winter 875 is only offered in Spring (775 in Au/Sp) CSE770 Relevant Courses

25. 788.xxx: 3 credit, letter-graded, once in two years 888.xxx: S/U graded, every quarter Agrawal: 788.11I, 888.11I Ferhatosmanoglu: 788.02H, 888.02H Lauria: 788.08R, 888.08R Panda: 788.08P, 888.08P Parthasarathy: 788.02G, 888.02J Sadayappan: 788.11J, 888.11J, 888.11K Specialty Courses

26. Addressing cutting-edge research challenges with focus on multi-disciplinary applications Significant research funding from federal, industrial and state sources Synergistic group with significant growth in the last few years Summary