Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing. Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more! Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required. The course also features interviews with leading researchers and managers, from both industry and academia.
Interview with William Gropp
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来自 University of Illinois at Urbana-Champaign 的课程
Cloud Computing Concepts, Part 1
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从本节课中
Week 2: Gossip, Membership, and Grids
Lesson 1: This module teaches how the multicast problem is solved by using epidemic/gossip protocols. It also teaches analysis of such protocols. Lesson 2: This module covers the design of failure detectors, a key component in any distributed system. Membership protocols, which use failure detectors as components, are also covered. Lesson 3: This module covers Grid computing, an important precursor to cloud computing.
与讲师见面
Indranil GuptaProfessor
Department of Computer Science
Hi there.
So we are here
in the, uh, Siebel Center for Computer Science
which is the Department of Computer Science
at the University of Illinois at Urbana-Champaign.
This is the building where I work
and a lot of other, uh, students
and a lot of other faculty work,
uh, here, in Computer Science.
So, uh, here we are talking
with, uh, Professor William Gropp.
And he is gonna tell us some juicy pieces of information
and opinions about, uh, uh, what he works on
and how that relates to cloud computing,
so, thank you for talking with us, uh, uh, Bill.
Uh, why don't you start by saying a little bit
about, uh, yourself?
Sure, so, uh, I am a-a professor here
in the Department of Computer Science.
I've been here for about seven years.
Before that, I was a scientist at Argonne National Lab,
and before that I was, uh, on the faculty at Yale.
Uh, I've been doing what we call high performance computing, uh,
since I was a graduate student,
and I think it's really cool and exciting.
Uh, one of the, uh, things I got to do just last year
was to be the general chair of the biggest meeting
in the high performance computing field
which has a little over 10,000 people attend.
So it was a really exciting time
in, uh, the Denver convention center, and so.
Uh, our field is very active, uh, very vibrant,
has a lot of cool opportunities,
uh, and has a lot of overlap with cloud computing,
so I'm happy to talk about it.
Alright, so you mention high performance computing, uh, w-can
w-can you say a little
about what high performance computing is?
Yeah, it's one of these things that's sorta hard to define.
It's like, you know it if you see it.
Uh, one way to define it
is it's computing where performance is important.
Uh, and then that's maybe the broadest definition.
Uh, definitions also used
is that it's computing that involves supercomputers,
or machines that are like supercomputers.
And so you could ask what a supercomputer is.
Uh, the definition of that has also changed over time,
but one way to define it
is it's about the most expensive computer you can buy.
[Indy laughs]
Um, it was once defined as a machine that cost $12 million
but there's been inflation since then.
Um, but a supercomputer is, is a computer that, uh,
allows you to tackle problems that are just oth-otherwise uh,
out of reach, and
a typical supercomputer or computer of that class,
is made up of thousands to hundreds of thousands
of the kind of fast processors you might find in a server node.
What are the similarities or the differences
between high performance computing and cloud computing,
as you see cloud computing?
So the, let me start with the similarities.
Um, in both cases, you're taking advantage, uh,
getting more performance
by having large numbers of processors
that can attack some collection of problems.
Maybe a problem, maybe a collection of problems.
The- maybe the biggest difference,
between high performance computing and cloud computing
and this is a difference
that is, uh, definitely a shade of gray,
is that in high performance computing,
well, first, as I mentioned,
the, uh, focus on performance, is quite important.
Many high performance computing problems require
the, uh, coordinated work of all of those processors
on this same problem at the same time.
Uh, high performance supercomputers tend to have, uh,
very high performance networks
that have low latency and high bandwidth
and these networks are significantly more powerful
than the ones you find in the cloud system,
there're also significantly more expensive. [laughs]
But they're needed for the kind of tight coordination.
I often use a-a symphony orchestra sort of analogy.
The kind of programs that we run in high performance computing
are often like symphonies,
in they have to be well-coordinated.
Y'know, maybe not by a single director,
but they have to work very closely together.
In fact, uh, anybody who's a m-musician knows
that, uh, even the stage for a symphony orchestra
has to be defined well
so that the musicians can hear each other well,
and keep things coordinated.
Um, clouds can do that under some circumstances,
but they're much more optimized towards independent work
that's, um, less frequently coordinated;
it's not uncoordinated, but less frequently.
And so you don't have to, uh, have the same tight control
of what's going on.
And so, these communities, uh,
HPC, high performance computing community
and the cloud computing committee, um,
are sort of, they sound very similar to each other
from what you're describing.
Can they learn from each other in terms of techniques?
Uh, uh, I think they can.
Uh, and definitely in both directions.
So, it's interesting
there was a recent paper from people at Google,
where they had discovered stuff
about, uh, performance irregularities in codes
that we have known in high performance computing
for decades.
Uh, and we also have solutions for the those problems. [laughs]
Uh, at the same time, uh, um,
the access model that clouds provide
is something that I find an increasing number
of computational scientists clamoring for,
the ability to get resources on demand
that scale to the size of their problem,
uh and that's requires a different way of thinking
about how you provide the access.
Um, I think also the, uh, a lot of high performance computing
has been designed around applications
that take advantage of the fact
that each of those compute elements
runs at the same speed as the other ones.
Uh, this is no longer true,
although, uh, a lot of applications
still think that it is,
and it's going to become less true over time.
And this is exactly the sort of situation
that cloud systems are already operating in, and so,
they, um, they've looked at that from some different viewpoints.
And finally, another issue that has been coming up
as we're looking at taking extreme scale computing
and high performance computing,
uh, beyond where we're at now
to systems that are a hundred or a thousand times faster
than current systems
is that it's gonna become increasingly difficult
to require that the system be completely reliable.
And, another thing is that,
uh, has been developed in cloud systems
is, uh, exploiting cheaper, less reliable components,
which made sense there,
but in the scientific computing you don't need to go cheaper.
But as we looked at the extreme scale systems,
it's gonna be increasingly expensive
to be, the sort of ultra-light reliable that you need.
And so again, there's an area where
what's going on in cloud computing
will offer some insights, maybe not to solutions,
because the problems have different characteristics,
but some insights on how to attack them.
So can you say a little bit about, uh,
what kinds of research problems you are working on now?
Sure, so, they're really in-in two groups, so,
one of them is a computational science problem.
So, I'm the lead PI for, uh,
the Center on Plasma Coupled Combustion, here,
which is funded by the Department of Energy.
What it's looking at is trying to understand, uh,
what is, uh, really a new way to control combustion
by using plasmas
and you might use this to, uh, hold onto the flame
in a hypersonic jet engine.
Where one of the big problems
is that the fact that it's hypersonic means
that it's very hard to keep the flame from being blown out.
Plasma gives you a way to hold onto the flame.
Uh, also allows you to guide where the combustion happens,
so you might be able to make a far cleaner,
more efficient burning internal combustion engine.
But the problem is, that, uh,
no-one really knows what's-how that works.
You know, you can do it in a lab, uh,
set up a little experiment and we can do that here,
it's really great,
and it's part of our, uh, project
as we have people who do these experiments.
We can set them up and they can do them
and they can measure stuff,
but if you want to design, uh, an engine that uses this,
um, you need to have some insight into what's going on.
You need to be able to do predictions about, uh,
how effective it's going to be,
you need something to help you optimize the design,
and for that, we-we do this by computing,
and the kind of computing
that's required for this is enormous,
because you hafta understand what's happening
on link scales that you can see in your hand,
and you need to understand what's happening at link scales
which are the atoms bouncing off the electrodes
that are creating the plasma, and everything in between.
And that's a problem that's too big for any one, uh, processor,
or even ten or a hundred processors.
And so, in fact, the Department of Energy is interested in this,
less because of the science, although it's interesting,
but more because they're interested
in understanding the techniques
both computer science techniques
the applied math techniques
to handle problems of this complexity,
which are really at-at the, uh, at the edge of what we can do.
And then, the other part of my research
is focused on say, the computer science part of that,
which is, how do I express those programs?
Uh, how do I write the programs so that it's efficient,
and it has to be efficient, on an individual node,
and it has to be efficient running across
a hundred thousand or a million nodes.
And, um, I think you'll be asking me a little more
about that later, so I'll-I'll wait for that.
But that's the two parts of my research at this time.
Okay, um, let's move on to a different topic, uh, MPI,
which is pretty close to your heart, uh.
You are of course, one of the inventors
of this very popular programming paradigm, MPI.
Can you say a little about what MPI is?
Sure, so, MPI's, um, very boringly stands
for Message Passing Interface.
Uh, it's a, uh, standard, if, well,
maybe sometimes called an ad hoc standard
because it wasn't an official organization behind it.
Uh, but it's a ad hoc standard
that codifies communicating sequential processes.
Uh, at least it started like that.
Over time it's added more and more, uh, techniques
for doing the kind of parallel computing
that we do in high performance computing.
So one of the features of MPI is it's designed
for reliable systems,
designed for systems, uh, at very large scale,
and I'll say a little bit about what that means in a minute,
uh, and it's designed for, uh, systems
that need to get the utmost in performance,
um, out of their applications.
Um, it's not a high level, easy to use necessarily model.
But it's been a very powerful and very flexible one.
Uh, and astoundingly, it's now over 20 years old,
which, uh, even I have trouble believing at times.
Uh, never expected it to succeed so well.
Um, to give everyone a sense of, um, the scale which it's used,
there are MPI programs now
that typic-that run on over a million processors.
[laughs]
Uh, so it scales, y'know, well past what, uh,
current cloud systems look like.
Uh, the cost of moving data from, uh,
user process to user process in this message passing model
is typically on the order of a microsecond or less
on, uh, a HPC system,
and the bandwidths for the data that's being moved,
uh, exceeds y'know, on this-on the-the slowest systems
it's several hundred megabytes per second,
and, uh, exceeds gigabytes per second, uh, on the best systems.
Uh, and the bisection band, with the ability of, um, say,
half the-half of those million processes
taught the other half a million processes,
uh, is often, on today's systems well over a terabyte per second,
uh, and all of that is achievable, uh, with MPI,
and MPI programs that were written 20 years ago,
some of them are still running today and doing good science,
which, um, I think speaks
to the flexibility and generality of the design.
And is MPI, would you say MPI is applicable in, uh,
data centers, and clouds as well?
Can the programming model be used just as easily there?
Yes, it is, and I mean, I run MPI programs on my laptop.
The-one assumption that MPI makes,
that is maybe awkward for some cloud systems,
is it does assume that there's reliable communication layer,
um, and it doesn't have built in facilities
to deal with, uh, a lack of reliability,
um, so that's an issue.
It also, uh, tends to assume a static group of processes, uh,
although there are features in it
to add and subtract processes,
but I would say that they're weaker
than what you might want if you were doing it a lot.
But, uh, for example, the reduction operation
that you might have talked to people about, uh,
MPI provides a number of different versions of that,
depending on how you are doing your reduction.
And those reduction, uh, im-those reduction operations
and the algorithms behind them had been tuned over the decades,
to be very, very fast,
um, there's some very clever ways to do that.
The same is true for the sort of reverse of that
to do a broadcast,
um, we can, um, move data
from one process to all other processes,
uh, pretty much at that terabyte per second rate.
But, and it sounds like from what you are saying,
that MPI could be combined with things like membership services,
which already exist,
had to-to build a system
that would be oriented more towards clouds?
Absolutely.
Absolutely.
And there are people who were running MPI on clouds now,
so it's not, uh, something that's terribly strange.
The, uh, the biggest thing is not so much MPI,
but the kind of applications that are written with MPI that,
as I mentioned before, tend to assume,
uh, uniform performance of their computing elements.
What were the motivating factors
behind coming up with the paradigm in the first place?
So the, uh, the situation when um, MPI was developed
was that there were a number of vendors,
uh, large and small, so, um,
start-up companies, um, IBM, uh, Intel, uh,
and they all had their own APIs,
their own programming interfaces.
Uh, mostly built around this communicating,
sequential processes model,
message processing model,
um, for writing these programs.
And, uh, Ken Kennedy is well known in parallel computing,
particularly in, uh, compilers,
was working on a par-, uh,
compiler for a parallel language
and he wanted to just have one API for,
for, as a compiler target.
And he got a bunch of us together, so us,
meaning people who were developing
one of these different interfaces.
And we all explained why they all had to be different,
and Ken, uh, said, this is great,
looks like we're all in agreement
that we can find a common standard.
Um, this is sort of a mark of a great man,
who, uh, uh, and he was right. [laughs]
Um, so over the next, uh, year, or so, uh, a proposal was made,
uh, many of us reacted to the proposal
by saying we have to do better than this.
Um, and I'm, should say, it was not a bad proposal,
but it wasn't good enough.
And a lot of people got together,
uh, the vendors sent their best people,
um, the research group sent their best people,
some application groups,
uh, who had the same sort of desire that Ken did,
they wanted to s- you know, they wanted to put their time
into writing a better application,
not importing from system to system.
And over about a year and a half,
we came up with a s- uh, standard,
and as part of that,
I had committed my group to developing and implementation,
so as we were developing the standard
we had something that ran, that we could,
explore and experiment with
and make sure we were making the right choices.
And, uh, that came up with what became the MPI1 standard,
um, we took advantage of, uh,
people who had been working on the standard
for high performance FORTRAN,
which was another ad hoc standard.
Uh, we even used the same hotel in north Dallas
which really encouraged you to s- um, stay there and work.
[laughs] Um, and, we developed a careful standard.
We got it published.
It was published by, uh, International Journal
of High Performance Computing Applications.
Um, we made it freely available
so, uh, you can download the pdf,
and you can do that now, with MPI3,
you can go to the MPI-forum.org website
and get the standard, so,
we don't charge any-anything for it.
And the- um, there was also an implementation available,
and, uh, with that, people could start using it.
We also wrote, uh some books, there's Using MPI, Using MPI2.
Um, Using MPI is in its second edition,
and its third edition
was sent to the publisher last month.
Um, another, um, thing that's hard to, uh, hard to believe
sometimes that it's been around that long.
Uh, and that produced a standard
that applications people could start programming to
and that meant that they had, they could no longer
they no longer had to worry about, um, spending time
every time a new parallel machine came out,
moving their code to it.
And, because we had in, had a very open process,
the meetings were open,
because we involved application developers,
as well as the vendors and as well as people
who were doing research
into these parallel programming systems,
we made sure that the design
was fast, efficient, and complete enough
so that over the years the application, uh, developers
have not really needed anything else.
They made one and a few other things,
and there is an MPI2 and an MPI3,
both of which have added features, but by and large,
uh, applications have been able to write
whatever they needed to in MPI.
And that's different than some other efforts where,
for example, uh, what was provided
was what the people doing the development provided,
but it wasn't, it didn't have the breadth,
didn't have the completeness
that was needed by the applications.
Sounds like, uh, standardization
was one of the motivating factors
for the development of MPI.
I-i-it was, and I-I wanna say that it wasn't the first effort
to standardize this kind of parallel programming.
Um, eh, one-one challenge for people
is standardization sounds great, sounds like, uh, you would,
it's amazing the number of people
who wanted to standardize something
because they look at the success of MPI
and they want to duplicate that success.
Uh, but what they forget was,
that there were several efforts
to standardize message passing before MPI that failed.
Um, and it's important to have a mature enough system,
a mature enough community of understanding the issues,
before you start to standardize, um, but yeah.
Once you have that knowledge,
then it's time to standardize and move on.
Well, um, that's the last question I have for you.
Thank you for, uh, taking time to talk with us.
It's my pleasure.
Thanks, Bill.
