Before you purchase any hardware, it may be a good idea to consider the design of your system. There are basically two hardware issues involved with design of a Beowulf system: the type of nodes or computers you are going to use; and way you connect the computer nodes. There is one software issue that may effect your hardware decisions; the communication library or API. A more detailed discussion of hardware and communication software is provided later in this document.
While the number of choices is not large, there are some important design decisions that must be made when constructing a Beowulf systems. Because the science (or art) of "parallel computing" has many different interpretations, an introduction is provided below. If you do not like to read background material, you may skip this section, but it is advised that you read section Suitability before you make you final hardware decisions.
This section provides background on parallel computing concepts. It is NOT an exhaustive or complete description of parallel computing science and technology. It is a brief description of the issues that may be important to a Beowulf designer and user.
As you design and build your Beowulf, many of these issues described below will become important in your decision process. Due to its component nature, a Beowulf Supercomputer requires that we consider many factors carefully because they are now under our control. In general, it is not all that difficult to understand the issues involved with parallel computing. Indeed, once the issues are understood, your expectations will be more realistic and success will be more likely. Unlike the "sequential world" where processor speed is considered the single most important factor, processor speed in the "parallel world" is just one of several factors that will determine overall system performance and efficiency.
Parallel computing can take many forms. From a user's perspective, it is important to consider the advantages and disadvantages of each methodology. The following section attempts to provide some perspective on the methods of parallel computing and indicate where the Beowulf machine falls on this continuum.
Answering this question is important. Using 8 CPUs to run your word processor sounds a little like "over-kill" -- and it is. What about a web server, a database, a rendering program, or a project scheduler? Maybe extra CPUs would help. What about a complex simulation, a fluid dynamics code, or a data mining application. Extra CPUs definitely help in these situations. Indeed, multiple CPUs are being used to solve more and more problems.
The next question usually is: "Why do I need two or four CPUs, I will just wait for the 986 turbo-hyper chip." There are several reasons:
If you need speed - either due to a compute bound problem and/or an I/O bound problem, parallel is worth considering. Because parallel computing is implemented in a variety of ways, solving your problem in parallel will require some very important decisions to be made. These decisions may dramatically effect portability, performance, and cost of your application.
Before we get technical, let's look take a look at a real "parallel computing problem" using an example with which we are familiar - waiting in long lines at a store.
Consider a big store with 8 cash registers grouped together in the front of the store. Assume each cash register/cashier is a CPU and each customer is a computer program. The size of the computer program (amount of work) is the size of each customer's order. The following analogies can be used to illustrate parallel computing concepts.
One cash register open (is in use) and must process each customer one at a time.
Computer Example: MS DOS
One cash register open, but now we process only a part of each order at a time, move to the next person and process some of their order. Everyone "seems" to be moving through the line together, but if no one else is in the line, you will get through the line faster.
Computer Example: UNIX, NT using a single CPU
Now we open several cash registers in the store. Each order can be processed by a separate cash register and the line can move much faster. This is called SMP - Symmetric Multi-processing. Although there are extra cash registers open, you will still never get through the line any faster than just you and a single cash register.
Computer Example: UNIX and NT with multiple CPUs
If you "break-up" the items in your order, you might be able to move through the line faster by using several cash registers at one time. First, we must assume you have a large amount of goods, because the time you invest "breaking up your order" must be regained by using multiple cash registers. In theory, you should be able to move through the line "n" times faster than before*; where "n" is the number of cash registers. When the cashiers need to get sub- totals, they can exchange information quickly by looking and talking to all the other "local" cash registers. They can even snoop around the other cash registers to find information they need to work faster. There is a limit, however, as to how many cash registers the store can effectively locate in any one place.
Amdals law will also limit the application speed-up to the slowest sequential portion of the program.
Computer Example: UNIX or NT with extra CPU on the same motherboard running multi-threaded programs.
In order to improve performance, the store adds 8 cash registers at the back of the store. Because the new cash registers are far away from the front cash registers, the cashiers must call on the phone to send their sub-totals to the front of the store. This distance adds extra overhead (time) to communication between cashiers, but if communication is minimized, it is not a problem. If you have a really big order, one that requires all the cash registers, then as before your speed can be improved by using all cash registers at the same time, the extra overhead must be considered. In some cases, the store may have single cash registers (or islands of cash registers) located all over the store - each cash register (or island) must communicate by phone. Since all the cashiers working the cash registers can talk to each other by phone, it does not matter too much where they are.
Computer Example: One or several copies of UNIX or NT with extra CPUs on the same or different motherboard communicating through messages.
The above scenarios, although not exact, are a good representation of constraints placed on parallel systems. Unlike a single CPU (or cash register) communication is an issue.
The common methods and architectures of parallel computing are presented below. While this description is by no means exhaustive, it is enough to understand the basic issues involved with Beowulf design.
There are basically two ways parallel computer hardware is put together:
A typical Beowulf is a collection of single CPU machines connected using fast Ethernet and is, therefore, a local memory machine. A 4 way SMP box is a shared memory machine and can be used for parallel computing - parallel applications communicate using shared memory. Just as in the computer store analogy, local memory machines (individual cash registers) can be scaled up to large numbers of CPUs, while the number of CPUs shared memory machines (the number of cash registers you can place in one spot) can have is limited due to memory contention.
It is possible, however, to connect many shared memory machines to create a "hybrid" shared memory machine. These hybrid machines "look" like a single large SMP machine to the user and are often called NUMA (non uniform memory access) machines because the global memory seen by the programmer and shared by all the CPUs can have different latencies. At some level, however, a NUMA machine must "pass messages" between local shared memory pools.
It is also possible to connect SMP machines as local memory compute nodes. Typical CLASS I motherboards have either 2 or 4 CPUs and are often used as a means to reduce the overall system cost. The Linux internal scheduler determines how these CPUs get shared. The user cannot (at this point) assign a specific task to a specific SMP processor. The user can however, start two independent processes or a threaded processes and expect to see a performance increase over a single CPU system.
There basically two ways to "express" concurrency in a program:
Other methods do exist, but these are the two most widely used. It is important to remember that the expression of concurrency is not necessary controlled by the underlying hardware. Both Messages and Threads can be implemented on SMP, NUMA-SMP, and clusters - although as explained below efficiently and portability are important issues.
Historically, messages passing technology reflected the design of early local memory parallel computers. Messages require copying data while Threads use data in place. The latency and speed at which messages can be copied are the limiting factor with message passing models. A Message is quite simple: some data and a destination processor. Common message passing APIs are PVM or MPI. Message passing can be efficiently implemented using Threads and Messages work well both on SMP machine and between clusters of machines. The advantage to using messages on an SMP machine, as opposed to Threads, is that if you decided to use clusters in the future it is easy to add machines or scale your application.
Operating system Threads were developed because shared memory SMP (symmetrical multiprocessing) designs allowed very fast shared memory communication and synchronization between concurrent parts of a program. Threads work well on SMP systems because communication is through shared memory. For this reason the user must isolate local data from global data, otherwise programs will not work properly. In contrast to messages, a large amount of copying can be eliminated with threads because the data is shared between processes (threads). Linux supports POSIX threads. The problem with threads is that it is difficult to extend them beyond one SMP machine and because data is shared between CPUs, cache coherence issues can contribute to overhead. Extending threads beyond the SMP boundary efficiently requires NUMA technology which is expensive and not natively supported by Linux. Implementing threads on top of messages has been done ( (http://syntron.com/ptools/ptools_pg.htm)), but Threads are often inefficient when implemented using messages.
The following can be stated about performance:
SMP machine cluster of machines scalability
performance performance
----------- ------------------- -----------
messages good best best
threads best poor* poor*
* requires expensive NUMA technology.
In order to run an application in parallel on multiple CPUs, it must be explicitly broken in to concurrent parts. A standard single CPU application will run no faster than a single CPU application on multiple processors. There are some tools and compilers that can break up programs, but parallelizing codes is not a "plug and play" operation. Depending on the application, parallelizing code can be easy, extremely difficult, or in some cases impossible due to algorithm dependencies.
Before the software issues can be addressed the concept of Suitability needs to be introduced.
Most questions about parallel computing have the same answer:
"It all depends upon the application."
Before we jump into the issues, there is one very important distinction that needs to be made - the difference between CONCURRENT and PARALLEL. For the sake of this discussion we will define these two concepts as follows:
CONCURRENT parts of a program are those that can be computed independently.
PARALLEL parts of a program are those CONCURRENT parts that are executed on separate processing elements at the same time.
The distinction is very important, because CONCURRENCY is a property of the program and efficient PARALLELISM is a property of the machine. Ideally, PARALLEL execution should result in faster performance. The limiting factor in parallel performance is the communication speed and latency between compute nodes. (Latency also exists with threaded SMP applications due to cache coherency.) Many of the common parallel benchmarks are highly parallel and communication and latency are not the bottle neck. This type of problem can be called "obviously parallel". Other applications are not so simple and executing CONCURRENT parts of the program in PARALLEL may actually cause the program to run slower, thus offsetting any performance gains in other CONCURRENT parts of the program. In simple terms, the cost of communication time must pay for the savings in computation time, otherwise the PARALLEL execution of the CONCURRENT part is inefficient.
The task of the programmer is to determining what CONCURRENT parts of the program SHOULD be executed in PARALLEL and what parts SHOULD NOT. The answer to this will determine the EFFICIENCY of application. The following graph summarizes the situation for the programmer:
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% of | *
appli- | *
cations | *
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communication time/processing time
In a perfect parallel computer, the ratio of communication/processing would be equal and anything that is CONCURRENT could be implemented in PARALLEL. Unfortunately, Real parallel computers, including shared memory machines, are subject to the effects described in this graph. When designing a Beowulf, the user may want to keep this graph in mind because parallel efficiency depends upon ratio of communication time and processing time for A SPECIFIC PARALLEL COMPUTER. Applications may be portable between parallel computers, but there is no guarantee they will be efficient on a different platform.
IN GENERAL, THERE IS NO SUCH THING AS A PORTABLE AND EFFICIENT PARALLEL PROGRAM
There is yet another consequence to the above graph. Since efficiency depends upon the comm./process. ratio, just changing one component of the ratio does not necessary mean a specific application will perform faster. A change in processor speed, while keeping the communication speed that same may have non- intuitive effects on your program. For example, doubling or tripling the CPU speed, while keeping the communication speed the same, may now make some previously efficient PARALLEL portions of your program, more efficient if they were executed SEQUENTIALLY. That is, it may now be faster to run the previously PARALLEL parts as SEQUENTIAL. Furthermore, running inefficient parts in parallel will actually keep your application from reaching its maximum speed. Thus, by adding faster processor, you may actually slowed down your application (you are keeping the new CPU from running at its maximum speed for that application)
UPGRADING TO A FASTER CPU MAY ACTUALLY SLOW DOWN YOUR APPLICATION
So, in conclusion, to know whether or not you can use a parallel hardware environment, you need to have some insight into the suitability of a particular machine to your application. You need to look at a lot of issues including CPU speeds, compiler, message passing API, network, etc. Please note, just profiling an application, does not give the whole story. You may identify a computationally heavy portion of your program, but you do not know the communication cost for this portion. It may be that for a given system, the communication cost as do not make parallelizing this code efficient.
A final note about a common misconception. It is often stated that "a program is PARALLELIZED", but in reality only the CONCURRENT parts of the program have been located. For all the reasons given above, the program is not PARALLELIZED. Efficient PARALLELIZATION is a property of the machine.
Once you decide that you need parallel computing and would like to design and build a Beowulf, a few moments considering your application with respect to the previous discussion may be a good idea.
In general there are two things you can do:
In either case, at some point you will need to look at the efficiency issues. In general, there are three things you need to do:
Let's look at these one at a time.
This step is often considered "parallelizing your program". Parallelization decisions will be made in step 2. In this step, you need to determine data dependencies.
From a practical standpoint, applications may exhibit two types of concurrency: compute (number crunching) and I/O (database). Although in many cases compute and I/O concurrency are orthogonal, there are application that require both. There are tools available that can perform concurrency analysis on existing applications. Most of these tools are designed for FORTRAN. There are two reasons FORTRAN is used: historically most number crunching applications were written in FORTRAN and it is easier to analyze. If no tools are available, then this step can be some what difficult for existing applications.
Without the help of tools, this step may require trial and error tests or just a plain old educated guess. If you have a specific application in mind, try to determine if it is CPU limited (compute bound) or hard disk limited (I/O bound). The requirements of your Beowulf may be quite different depending upon your needs. For example, a compute bound problem may need a few very fast CPUs and high speed low latency network, while an I/O bound problem may work better with more slower CPUs and fast Ethernet.
This recommendation often comes as a surprise to most people because, the standard assumption is that faster processor are always better. While this is true if your have an unlimited budget, real systems may have cost constraints that should be maximized. For I/O bound problems, there is a little known rule (called the Eadline-Dedkov Law) that is quite helpful:
For two given parallel computers with the same cumulative CPU performance index, the one which has slower processors (and a probably correspondingly slower interprocessor communication network) will have better performance for I/O-dominant applications.
While the proof of this rule is beyond the scope of this document, you find it interesting to download the paper Performance Considerations for I/O-Dominant Applications on Parallel Computers (Postscript format 109K ) (ftp://www.plogic.com/pub/papers/exs-pap6.ps)
Once you have determined what type of concurrency you have in your application, you will need to estimate how efficient it will be in parallel. See Section Software for a description of Software tools.
In the absence of tools, you may try to guess your way through this step. If a compute bound loop measured in minutes and the data can be transferred in seconds, then it might be a good candidate for parallelization. But remember, if you take a 16 minute loop and break it into 32 parts, and your data transfers require several seconds per part, then things are going to get tight. You will reach a point of diminishing returns.
There are several ways to describe concurrent parts of your program:
The major difference between the two is that explicit parallelism is determined by the user where implicit parallelism is determined by the compiler.
These are basically method where the user must modify source code specifically for a parallel computer. The user must either add messages using PVM or MPI or add threads using POSIX threads. (Keep in mind however, threads can not move between SMP motherboards).
Explicit methods tend to be the most difficult to implement and debug. Users typically embed explicit function calls in standard FORTRAN 77 or C/C++ source code. The MPI library has added some functions to make some standard parallel methods easier to implement (i.e. scatter/gather functions). In addition, it is also possible to use standard libraries that have been written for parallel computers. Keep in mind, however, the portability vs. efficiently trade-off)
For historical reasons, most number crunching codes are written in FORTRAN. For this reasons, FORTRAN has the largest amount of support (tools, libraries, etc.) for parallel computing. Many programmers now use C or re- write existing FORTRAN applications in C with the notion the C will allow faster execution. While this may be true as C is the closest thing to a universal machine code, it has some major drawbacks. The use of pointers in C makes determining data dependencies extremely difficult. Automatic analysis of pointers is extremely difficult. If you have an existing FORTRAN program and think that you might want to parallelize it in the future - DO NOT CONVERT IT TO C!
Implicit methods are those where the user gives up some (or all) of the parallelization decisions to the compiler. Examples are FORTRAN 90, High Performance FORTRAN (HPF), Bulk Synchronous Parallel (BSP), and a whole collection of other methods that are under development.
Implicit methods require the user to provide some information about the concurrent nature of their application, but the compiler will then make many decisions about how to execute this concurrency in parallel. These methods provide some level of portability and efficiency, but there is still no "best way" to describe a concurrent problem for a parallel computer.