Arguably one of the most fundamental discipline that touches all other disciplines and people
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2 The scientific and mathematical approach in information technology and computing Started in the 1960s from Mathematics or Electrical Engineering Today: Arguably one of the most fundamental discipline that touches all other disciplines and people Introduction to Computer Science: The What, How, and Why of CS 2
3 The advent of computation can be compared, in terms of the breadth and depth of its impact on research and scholarship, to the invention of writing and the development of modern mathematics. Ian Foster, 2006 Introduction to Computer Science: The What, How, and Why of CS 3
4 Introduction to Computer Science: The What, How, and Why of CS 4
5 Theory Theory of computation Information and coding theory Algorithms and data structures Programming language theory Formal methods Systems Introduction to Computer Science: The What, How, and Why of CS 5
6 Theory Systems Artificial intelligence Computer architecture Computer graphics and visualization Computer security and cryptography Computational science Databases and information retrieval Distributed systems Health Informatics Information science Programming Languages Software engineering Introduction to Computer Science: The What, How, and Why of CS 6
7 What is a distributed system? A collection of independent computers that appears to its users as a single coherent system -A. Tanenbaum Introduction to Computer Science: The What, How, and Why of CS 11
8 Introduction to Computer Science: The What, How, and Why of CS 12 [GCE08] Cloud Computing and Grid Computing 360-Degree Compared
9 Computer clusters using commodity processors, network interconnects, and operating systems. Introduction to Computer Science: The What, How, and Why of CS 13
10 Rack 32 Node Cards Supercomputing ~ HPC Cabled 8x8x16 Baseline System 32 Racks Chip 4 processors Compute Card 1 chip, 1x1x1 Node Card (32 chips 4x4x2) 32 compute, 0-4 IO cards 435 GF/s 64 GB 14 TF/s 2 TB 500TF/s 64 TB Highly-tuned computer clusters using commodity 13.6 GF/s 13.6 GF/s processors 2 GB DDR combined with custom network 8 MB EDRAM interconnects and customized operating system Introduction to Computer Science: The What, How, and Why of CS 14
11 Grids tend to be composed of multiple clusters, and are typically loosely coupled, heterogeneous, and geographically dispersed NCAR UC/ANL NCSA PU IU PSC Tennessee 2008 (~1PF) ORNL SDSC Tommy Minyard, TACC TACC 2007 (504TF) LONI/LSU Grids ~ Federation Computational Resources Introduction to Computer Science: The What, (size How, approximate and Why - of not CS to scale) 15
12 A large-scale distributed computing paradigm driven by: 1. economies of scale 2. virtualization 3. dynamically-scalable resources 4. delivered on demand over the Internet Clouds ~ hosting Introduction to Computer Science: The What, How, and Why of CS 16
13 Introduction to Computer Science: The What, How, and Why of CS 17
14 CS 447 Introduction to Distributed Systems CS 546 Parallel and Distributed Processing CS 550 Advanced Operating Systems CS 552 Distributed Real-Time Systems CS 553 Cloud Computing CS 570 Advanced Computer Architecture CS 595 Data-Intensive Distributed Computing Introduction to Computer Science: The What, How, and Why of CS 18
15 Xian-He Sun Zhiling Lan Shangping Ren Ioan Raicu Introduction to Computer Science: The What, How, and Why of CS 19
16 Research Focus Emphasize designing, implementing, and evaluating systems, protocols, and middleware with the goal of supporting data-intensive applications on extreme scale distributed systems, from many-core systems, clusters, grids, clouds, and supercomputers People Dr. Ioan Raicu (Director) 5 PhD Students 4 MS Students 2 UG Students Contact iraicu@cs.iit.edu
17 Number of Cores Manufacturing Process Number ~1000 cores/threads of Cores commodity architectures 20 Processing Pat Helland, Microsoft, The Irresistible Forces Meet the Movable Objects, November 9 th, 2007 Introduction to Computer Science: The What, How, and Why of CS 100 Today (2011): Multicore Computing 80 O(10) cores commodity architectures O(100) cores proprietary architectures 50 O(1000) GPU hardware threads 40 Near future (~2018): Manycore Computing
18 Top500 Core-Max (# of cores) Top500 Core-Sum (# of cores) 300, ,000 Top500 Core-Max Top500 Core-Sum 5,000,000 4,000, , , ,000 50, ,000,000 Today (2011): Petascale Computing O(10K) nodes O(100K) cores 2,000,000 1,000,000 ~1M nodes (100X) 0 Near future (~2018): Exascale Computing ~1B processor-cores/threads (10000X) Top500 Projected Development, 22
19 Relatively new paradigm 3 years old Amazon in K servers split over 6 zones 320K-cores, 320K disks $100M costs + $12M/year in energy costs Revenues about $250M/year Amazon in 2018 Will likely look similar to exascale computing 100K~1M nodes, ~1B-cores, ~1M disks $100M~$200M costs + $10M~$20M/year in energy Revenues 100X~1000X of what they are today Introduction to Computer Science: The What, How, and Why of CS 23
20 Power efficiency Will limit the number of cores on a chip (Manycore) Will limit the number of nodes in cluster (Exascale and Cloud) Will dictate a significant part of the cost of ownership Programming models/languages Automatic parallelization Threads, MPI, workflow systems, etc Functional, imperative Languages vs. Middlewares Introduction to Computer Science: The What, How, and Why of CS 24
21 Bottlenecks in scarce resources Storage (Exascale and Clouds) Memory (Manycore) Reliability How to keep systems operational in face of failures Checkpointing (Exascale) Node-level replication enabled by virtualization (Exascale and Clouds) Hardware redundancy and hardware error correction (Manycore) Introduction to Computer Science: The What, How, and Why of CS 25
22 Tasks Completed Number of Processors Throughput (tasks/sec) CPU Cores: Tasks: Elapsed time: 2483 secs CPU Years: Processors Active Tasks Tasks Completed Throughput (tasks/sec) Speedup: X (ideal ) Efficiency: 88% Time (sec) [SC08] Towards Loosely-Coupled Programming on Petascale Systems 26 Introduction to Computer Science: The What, How, and Why of CS
23 PDB protein descriptions ZINC 3-D structures 1 protein (1MB) 6 GB 2M structures (6 GB) FRED Manually prep DOCK6 rec file start DOCK6 Receptor (1 per protein: defines pocket to bind to) DOCK6 Manually prep FRED rec file FRED Receptor (1 per protein: defines pocket to bind to) ~4M x 60s x 1 cpu ~60K cpu-hrs NAB Script Template BuildNABScript NAB Script NAB script parameters (defines flexible residues, #MDsteps) Amber prep: 2. AmberizeReceptor 4. perl: gen nabscript report Select best ~5K Select best ~5K Amber Select best ~500 GCMC end ligands [SC08] Towards Loosely-Coupled Programming on Petascale Systems complexes ~10K x 20m x 1 cpu ~3K cpu-hrs ~500 x 10hr x 100 cpu ~500K cpu-hrs Amber Score: 1. AmberizeLigand 3. AmberizeComplex 5. RunNABScript For 1 target: 4 million tasks 500,000 cpu-hrs (50 cpu-years) 27 Introduction to Computer Science: The What, How, and Why of CS
![3% [SC08] Towards Loosely-Coupled](/docs-images/84/90843828/images/24-5.jpg "Programming on Petascale Systems 28")
24 CPU cores: Tasks: Elapsed time: 2.01 hours Compute time: CPU years Average task time: 667 sec Relative Efficiency: 99.7% (from 16 to 32 racks) Utilization: Sustained: 99.6% Overall: 78.3% [SC08] Towards Loosely-Coupled Programming on Petascale Systems 28 Introduction to Computer Science: The What, How, and Why of CS
25 Purpose On-demand stacks of random locations within ~10TB dataset Challenge Processing Costs: O(100ms) per object Data Intensive: 40MB:1sec Rapid access to 10-10K random files Time-varying load [DADC08] Accelerating Large-scale Data Exploration through Data Diffusion [TG06] AstroPortal: A Science Gateway for Large-scale Astronomy Data Analysis AP = Sloan Data Locality Number of Objects Number of Files Introduction to Computer Science: 29 The What, How, and Why of CS
26 Be the one creating and shaping the future of technology, not just the user Employment at the best technology companies in the world (see next slide) Be the next Steve Jobs (Apple), Bill Gates (Microsoft), Sergei Brin (Google), or Zach Zuckerberg (Facebook) Be part of the most amazing revolution to date: The Computing Revolution! Introduction to Computer Science: The What, How, and Why of CS 30
27 Google Yahoo Microsoft Amazon IBM Apple VMWare Netflix Cray Intel NVIDIA Facebook LinkedIn Salesforce.com Rackspace Red Hat Cleversafe UnivaUD Greenplum AsterData Proprietary Trading Companies Department of Energy Laboratories NASA Academic supercomputer centers Many more Introduction to Computer Science: The What, How, and Why of CS 31
28 More information: Contact: Questions? Introduction to Computer Science: The What, How, and Why of CS 32
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The advent of computation can be compared, in terms of the breadth and depth of its impact on research and scholarship, to the invention of writing and the development of modern mathematics. Ian Foster,
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