Building a Low-Cost Supercomputer by Dr. Tim McGuire - ACET 2000 Austin, TX

1. NO Building a Low-Cost Supercomputer Dr. Tim McGuire Sam Houston State University ACET 2000 Austin, TX

2. Acknowledgments • Most treatments of cluster computing (including this one) are heavily based on the seminal work of Greg Pfister (IBM Research, Austin,) In Search of Clusters • The concept of Beowulf clusters originated with Donald J. Becker and Thomas Sterling at the Center of Excellence in Space Data and Information Sciences, NASA Goddard Space Flight Center

3. Introduction • There are three ways to do anything faster: • Work harder • "Crunch Time" is familiar to all of us • Work smarter • Better to find a way to reduce the work needed • Get help • Certainly works, but we all know about committees ...

4. In a computer ... • Working Harder Get a faster processor • Working Smarter Use a better algorithm • Getting Help Parallel processing

5. Working Harder -- Faster Processors • The effect of faster processors is astonishing • The effective speed of the x86 family of processors has increased nearly 50% per year • RISC architectures have sustained a 60% annual cumulative growth rate • These trends will likely continue for the foreseeable future

6. Working Smarter -- Better Algorithms • The increases in speed made possible by better algorithms dwarf the accomplishments of faster hardware • Binary search on 1 billion items takes 30 comparisons, versus a maximum of one billion comparisons using linear search

7. Getting Help -- Parallel Processing • Covert parallel processing pipelining, vector processing, etc. really equivalent to faster hardware • Overt parallelism Done via software • "Parallelism is the wave of the future -- and always will be"

8. Early Attempts at Parallelism • Von Neumann thought it was too hard, and gave us the "Von Neumann bottleneck" • 60's ILLIAC IV project was the first great attempt at parallel processing (as well as trying to advance circuit and software technology.) • Japanese Fifth Generation Project launched another wave, including the Grand Challenge problems

9. Microprocessor Revolution • Microprocessors have had a superior price/performance ratio • "All you have to do is gang a whole bunch of them together" • The problem is "All you also have to do is program them to work together" • Programming costs much more than hardware

10. Highly Parallel Computing • Finally, (early 90's) microprocessors became fast and powerful enough that a practical-sized aggregation of them seemed the only feasible way to exceed supercomputer speeds • Even Cray Research (T3D) got into the act

11. "Lowly" Parallel Processing • Mid-to-late 90's -- military downsizing (among other things) caused funding to dry up • However … • Microprocessors kept getting faster … a lot faster • With overall performance doubling each year, in 4 years what needed 256 processors can be done with 16 instead. • System availability became a mass market issue • Since computers are so cheap, buy two (or more) for redundancy in case one fails and use them both, interconnected by a network

12. SMP -- One Form of "Cheap" Parallelism • Symmetric multiprocessors have been around for some time and have certain advantages over clusters • Typically, these have been shared memory systems -- few communication problems

13. The Big Distinction -- Programming • How you program SMP systems is substantially different from programming clusters: Their programming models are different • If you explicitly exploit SMP in an application, it's essentially impossible to efficiently exploit clusters in the same program

14. Why Clusters? • The Standard Litany • Why Now ? • Why Not Now?

15. The Standard Litany • Performance • Availability • Price/Performance Ratio • Incremental Growth • Scaling • Scavenging

16. Performance • No matter what form or measure of performance one is seeking -- throughput, response time, turnaround time, etc., it is straightforward to claim that one can get even more of it by using a bunch of machines at the same time. • Only occasionally does one hear the admission that a "tad bid" of new programming will be needed for anything to work correctly.

17. Availability • Having a computer shrivel up into an expensive paperweight can be a lot less traumatic if it's not unique, but rather one of a herd. • The work done by the dear departed sibling can be redistributed among the others (fail-soft computing)

18. Price/Performance Ratio • Clusters and other forms of computer aggregation are typically collections of machines that individually have very good performance for their price. • The promise is that the aggregate retains the price/performance of its individual members.

19. Incremental Growth • To the degree that one really does attain greater performance and availability with a group of computers, one should be able to enhance both by merely adding more machines. • Replacing machines should not be necessary.

20. Scaling • "Scalable" is, unfortunately, a buzzword • What it does deal with is how big a computer system can usably get. • It is a crucial element in the differentiation between clusters and symmetric multiprocessors.

21. Scavenging • "Look at all those unused CPU cycles spread across all the desktops in our network…" • Unused cycles are free. • However, how do you get and manage them? -- this complicates cluster support very significantly

22. The Benefits are Real • But, how does one take advantage of it? • The hardware provides the potential. • The fulfillment lies in the software, and unfortunately, software isn't riding the exponential growth curve.

23. Why Now? • Three Trends • Fat Boxes -- very high performance microprocessors • Fat Pipes -- standard high-speed communication • Thick Glue -- standard tools for distributed computing • One Market Requirement • High Availability

24. Fat Boxes • Microprocessors have kept, and will keep getting faster. • Supercomputers in the classic style are extinct for practical purposes • Mass-market, inexpensive microprocessors have crawled up the tailpipe of the workstation market just like workstations crawled up the tailpipe of minicomputers and mainframes earlier. • There are no more supercomputers, there is only supercomputing.

25. Fat Pipes • Commodity off the shelf (COTS) networking parts have achieved communication performance that was only previously possible with expensive, proprietary techniques • Standardized communication facilities such as • ATM - Asynchronous Transmission Mode • Switched Gigabit Ethernet • FCS -- Fibre Channel Standard • Performance of Gigabytes per second are possible.

26. Thick Glue • Standard tools for distributed computing such as TCP/IP • Intranets, the Internet, and the World Wide Web • Tool sets for distributed system administration • PVM (Parallel Virtual Machine) and MPI (Message Passing Interface)

27. Requirement for High Availability • Nobody has ever wanted computers to break. • However, never before has high availability become a significant issue in a mass market computer arena. • Clusters are uniquely capable of answering the need of both sides of the spectrum and are much cheaper than hardware based fault-tolerant approaches.

28. Why Not Now? • If they're so good, why haven't clusters become the most common mode of computation? • Lack of "single system image" software • Limited exploitation

29. Lack of Single System Image Software • Replacing a single large computer with a cluster means that many systems will have to be managed rather than one. • Their distributed management tools are tools, not turnkey systems • 50% of the cost of a computer system is staffing, rather than hardware, software, or maintenance

30. Limited Exploitation • Only relatively few types of subsystems now exploit the ability of clusters to provide both scalable performance and high availability. • This is a direct result of substantial difficulties that arise in parallel programming. • The problem is not hardware, it's software

31. An Exception • For one kind of parallel system, the software issues have been addressed to a large degree: The symmetric multiprocessor (SMP) • It of necessity requires a single system image

32. Definitions, Distinctions, and Comparisons • Definition • Distinction from Parallel Systems • Distinctions from Distributed Systems • Comparisons and Contrasts

33. Definition • A cluster is a type of parallel or distributed system that: • consists of a collection of interconnected stand-alone computers, and • is used as a single, unified computing resource • We define them as a subparadigm of distributed (or parallel) systems

34. Distinction from Parallel Systems • A useful analogy: • This is A Dog • (a single computer)

35. A Pack of Dogs • And this is a pack of dogs (running in parallel) • (a cluster)

36. A Savage Multiheaded Pooch • … or, pardon the abbreviation, "SMP" • (This pooch is no relation to Kerberos (Cerberus in Latin) that guards both the gates of Hades and distributed systems -- He only has three heads.)

37. Dog Packs and SMPs are Similar • Both are more potent than just plain dogs • They can both bring down larger prey than a plain single dog. • They eat more and eat faster than a single dog

38. Dog Packs and SMPs are Different • Scaling • Availability • System Management • Software Licensing

39. Scaling Differences • The Savage Multiheaded Pooch can take many bites at once • What happens when it tries to swallow? • It needs a larger throat, stomach, intestines, etc. • Similarly, to scale SMPs, you must beef up the entire machine • When you add another dog to a dog pack, you add a whole dog. You don't have to do anything to the other dogs. • Likewise, clusters

40. Availability • If an SMP breaks a leg … "that dog won't hunt" … no matter how many heads it has. • If a member of the pack is injured, the rest of the pack can still bring down prey.

41. System Management • You only have to walk a SMP once. • It takes a good deal more effort to train a pack of dogs to behave. • With the SMP, all you have to do is get the heads to learn basic cooperation (and that should be built into the operating system.)

42. Licensing (Dogs or Software) • If you get a license for an SMP, you'll probably only need one license • For an cluster of dogs, you'll need one per dog

43. Distinctions from Distributed Systems • The distinctions of clusters from distributed systems is not as clear (and a lot of people confuse the two.) • We'll try. The salient points are: • Internal Anonymity • Peer Relationship • Clusters as part of a Distributed System

44. Internal Anonymity • Nodes in a distributed system necessarily retain their own individual identities • The elements of a cluster are usually viewed from outside the cluster as anonymous • Internally, they may be differentiated, but externally the jobs are submitted to the cluster, not, for example, to cluster node #4

45. Peer Relationship • Distributed systems • use an underlying communication layer that is peer-to-peer • at a higher level, they are often organized into a client-server paradigm • Clusters • underlying communication is peer-to peer • organization is also peer-to-peer (with some minor exceptions)

46. Clusters as part of a Distributed System • Clusters usually exist in the context of a distributed system • In this case, they are viewed by the distributed system as a single node • For example, the cluster could server as a compute engine • It also could serve as, say, a DBMS server in the client-server paradigm (but that's not the organization we want to consider in this presentation)

47. Beowulf Clusters • The Beowulf project was initiated in 1994 under the sponsorship of the NASA HPCC program to explore how computing could be made "cheaper better faster". • They termed this PoPC -- a Pile of PCs

48. The "Pile of PCs" Approach • Very similar to COW (cluster of workstations) and shares the roots of NOW (network of workstations,) but emphasizes: • COTS (commodity off the shelf) components • dedicated processors (rather than scavenging cycles from idle workstations) • a private system area network (enclosed SAN rather than exposed LAN)

49. What Beowulf Adds • Beowulf adds to the PoPC model by emphasizing • no custom components • easy replication from multiple vendors • scalable I/O • a freely available software base • using freely available distributed computing tools with minimal changes • a collaborative design

50. Advantages of the Beowulf Approach • No single vendor owns the rights to the product -- not vulnerable to single vendor decisions • Approach permits technology tracking -- using the best, most recent components at the best price • Allows "just in place" configuration -- permits flexible and user driven decisions