HPF: Programming Linux Clusters the Easy Way
Many programmers use Linux as their operating system of choice when developing applications software. Increasingly, they run the application on the same PC when it is developed. But what do they do if the application becomes too large or runs too long?
The obvious answer is to run it on a cluster of PCs, possibly linked by Ethernet. To do this, you need to write code for each processor. In principle, different code for each, but usually there is common code for all except a selected one or two. The code must include data passing from processor to processor as required. This type of coding uses a “message-passing paradigm” and is in common use on multiprocessor mainframes and workstation clusters. Standard message-passing libraries, PVM (parallel virtual machine) and MPI (message-passing interface), exist to ensure portability.
Writing message-passing code is much harder than writing serial code, and it is easy to show why. Most scientific and engineering programming makes heavy use of one and two dimensional arrays, so we add two vectors this way:
DO I = 1,10000 a(i) = b(i) + c(i) END DO
This is clearly a parallel operation: all the elements of b and c can be added in parallel. For this to occur on a PC or workstation cluster, or indeed any distributed-memory parallel system, the elements of b, c and, most likely, a must be distributed over the available processors. The programmer must arrange this in his code by dealing with bits of each vector on each processor and keeping track of the bits.
A better solution would be for the compiler to do the arranging for you. Then your code could still refer to the complete vector or matrix object and thereby be easier to write, understand and maintain.
The advantages of compiler-aided parallel programming are widely accepted. In the scientific/engineering community, these advantages have led to the development of a standard parallel language called HPF, High Performance FORTRAN, and of HPF compilers for a widening range of architectures.
For good reasons, HPF is deliberately based on the latest, greatly upgraded, version of FORTRAN: FORTRAN95. Beginning with FORTRAN90, FORTRAN contains a rich variety of array facilities so that the loop above becomes the one line:
a = b + c
which is easier for a compiler, as well as a human, to understand.
Compilers do have difficulty deciding how best to distribute arrays across the processors; thus, HPF gives the programmer a way of providing help. Given that help, the compiler can fully automate the production of a data parallel program from a single FORTRAN77/90/95 source.
HPF codes are portable across SIMD, MIMD shared memory and MIMD Distributed Memory architectures. In particular, you can use HPF on a Linux PC cluster.
Here is a concocted example to demonstrate the facilities provided by HPF:
REAL a(1000), b(1000), c(1000), & x(500),y(0:501) !HPF$ PROCESSORS procs(10) !HPF$ DISTRIBUTE(BLOCK) ONTO procs :: a, b !HPF$ DISTRIBUTE(CYCLIC) ONTO procs :: c !HPF$ ALIGN x(i) WITH y(i+1) ... a(:) = b(:) ! Statement 1 x(1:500) = y(2:501) ! Statement 2 a(:) = c(:) ! Statement 3 ...
The lines starting with !HPF are HPF directives; the rest are standard FORTRAN90 and carry out three array operations. What are the directives doing?
The PROCESSORS directive specifies a linear arrangement of 10 virtual processors, which are mapped to the available physical processors in a manner not specified by the language. You might expect to need at least ten physical processors, but most HPF compilers will run the code happily on one or more (up to ten) physical processors. Grids of processors in any number of dimensions up to seven can be defined. They should match the problem being solved in some way—perhaps by helping to minimize communication costs. The processor directive is optional; the number of processors to use can be specified at runtime.
The DISTRIBUTE directives tell (actually, recommend to) the compiler how to distribute the elements of the arrays. Arrays a, b are distributed with blocks of 100 contiguous elements per processor, while c is distributed so that, for example, c(1), c(11), c(21), ... are on processor procs(1) and so on.
Note that the distribution of the arrays x and y is not specified explicitly, while the way they are aligned to each other is specified. The ALIGN statement causes x(i) and y(i+1) to be stored on the same processor for all values of i, regardless of the actual distribution.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide