Visit additional Tabor Communication Publications
May 20, 2009
Parallel programming uses threads to enable multiple operations to proceed simultaneously. The entire concept of parallel programming centers on the design, development and deployment of threads within an application and the coordination between threads and their respective operations. This article examines how to break up programming tasks into chunks that are suitable for threading.
In traditional programming models, such as object oriented programming (OOP), a program begins at a defined point (the main() function usually) and works through a series of tasks in succession. If the program relies on user interaction, the main processing instrument is a loop in which user events are handled; the program performs an established sequence of actions that ultimately ends with a wait for the next user action.
When designing such programs, developers enjoy a relatively simple programming world because only one thing is happening at any given moment. At any point in the process, one step generally flows into the next, leading up to a predictable conclusion, based on predetermined parameters.
To move from this linear model to a parallel programming model, designers must rethink the idea of process flow and design for threads. Rather than being constrained by a sequential execution sequence, programmers should identify those activities that can be executed in parallel. To do so, they must see their programs as a set of tasks with dependencies between them. Breaking programs down into these individual tasks and identifying dependencies is known as decomposition. A problem may be decomposed in several ways: by task, by data, or by data flow. Table 1 summarizes these forms of decomposition.
Table 1 Summary of the Major Forms of Decomposition
Decomposing a program by the functions that it performs is called task decomposition. It is one of the simplest ways to achieve parallel execution. Using this approach, individual tasks are cataloged. If two of them can run concurrently, they are scheduled to do so by the developer. Running tasks in parallel this way usually requires slight modifications to the individual functions to avoid conflicts and to indicate that these tasks are no longer sequential.
If we were discussing gardening, task decomposition would suggest that gardeners be assigned tasks based on the nature of the activity: if two gardeners arrived at a client's home, one might mow the lawn while the other weeded. Mowing and weeding are separate functions broken out as such. To accomplish them, the gardeners would make sure to have some coordination between them, so that the weeder is not sitting in the middle of a lawn that needs to be mowed.
Data decomposition, also known as data-level parallelism, breaks down tasks by the data they work on rather than by the nature of the task. Programs that are broken down via data decomposition generally have many threads performing the same work, just on different data items. For example, consider recalculating the values in a large spreadsheet. Rather than have one thread perform all the calculations, data decomposition would suggest having two threads, each performing half the calculations, or n threads performing 1/nth the work.
If the gardeners used the principle of data decomposition to divide their work, they would both mow half the property and then both weed half the flower beds. As in computing, determining which form of decomposition is more effective depends a lot on the constraints of the system. For example, if the area to mow is so small that it does not need two mowers, that task would be better done by just one gardener -- that is, task decomposition is the best choice -- and data decomposition could be applied to other task sequences, such as when the mowing is done and both gardeners begin weeding in parallel.
As the number of processor cores increases, data decomposition allows the problem size to be increased. This allows for more work to be done in the same amount of time. To illustrate, consider the gardening example. Two more gardeners are added to the work crew. Rather than assigning all four gardeners to one yard, we can we can assign the two new gardeners to another yard, effectively increasing our total problem size. Assuming that the two new gardeners can perform the same amount of work as the original two, and that the two yard sizes are the same, we've doubled the amount of work done in the same amount of time.
Many times, when decomposing a problem, the critical issue isn't what tasks should do the work, but how the data flows between the different tasks. In these cases, data flow decomposition breaks up a problem by how data flows between tasks.
The producer/consumer problem is a well known example of how data flow impacts a programs ability to execute in parallel. Here, the output of one task, the producer, becomes the input to another, the consumer. The two tasks are performed by different threads, and the second one, the consumer, cannot start until the producer finishes some portion of its work.
Using the gardening example, one gardener prepares the tools--that is, he puts gas in the mower, cleans the shears, and other similar tasks -- for both gardeners to use. No gardening can occur until this step is mostly finished, at which point the true gardening work can begin. The delay caused by the first task creates a pause for the second task, after which both tasks can continue in parallel. In computer terms, this particular model occurs frequently.
In common programming tasks, the producer/consumer problem occurs in several typical scenarios. For example, the results of the file I/O become the input to the next step, which might be threaded. However, that step cannot begin until the reading is either complete or has progressed sufficiently for other processing to kick off. Another common programming example is parsing; an input file must be parsed, or analyzed semantically, before the back-end activities, such as code generation in a compiler, can begin.
The producer/consumer problem has several interesting dimensions. The dependence created between consumer and producer can cause significant delays if this model is not implemented correctly. A performance-sensitive design seeks to understand the exact nature of the dependence and diminish the delay it imposes. It also aims to avoid situations in which consumer threads are idling while waiting for producer threads. In the ideal scenario, the hand-off between producer and consumer is completely clean, as in the example of the file parser. The output is context-independent and the consumer has no need to know anything about the producer. Many times, however, the producer and consumer components do not enjoy such a clean division of labor, and scheduling their interaction requires careful planning. If the consumer is finishing up while the producer is completely done, one thread remains idle while other threads are busy working away. This issue violates an important objective of parallel processing, which is to balance loads so that all available threads are kept busy. Because of the logical relationship between these threads, it can be very difficult to keep threads equally occupied.
Different decompositions provide different benefits and have various implications. If the goal, for example, is ease of programming and tasks can be neatly partitioned by functionality, then task decomposition is more often than not the winner. Data decomposition adds some additional code-level complexity to tasks, so it is reserved for cases where the data is easily divided and performance is important.
The most common reason for threading an application is performance. And in this case, the choice of decompositions is more difficult. In many instances, the choice is dictated by the problem domain; some tasks are much better suited to one type of decomposition. But some tasks have no clear bias. To return to the analogy of the gardeners, the decision would take this form: If two gardeners need to mow two lawns and weed two flower beds, how should they proceed? Should one gardener only mow -- that is, they choose task decomposition -- or should both gardeners mow together then weed together?
In some cases, the answer emerges quickly -- for instance when a resource constraint exists, such as only one mower. In others where each gardener has a mower, the answer comes only through careful analysis of the constituent activities. In the case of the gardeners, task decomposition looks better because the start-up time for mowing is saved if only one mower is in use. Ultimately, you determine the right answer for your application's use of parallel programming by careful planning and testing. The empirical timing and evaluation plays a more significant role in the design choices you make in parallel programming than it does in standard single-threaded programming.
The use of threads enables you to improve performance significantly by allowing two or more activities to occur simultaneously. However, developers cannot fail to recognize that threads add a measure of complexity that requires thoughtful consideration to navigate correctly. This complexity arises from the inherent fact that more than one activity is occurring in the program. Managing simultaneous activities and their possible interaction leads you to confronting four types of challenges. First is the synchronization, issue i.e. the process by which two or more threads coordinate their activities. For example, one thread waits for another to finish a task before continuing. Second is communication, which relates to the bandwidth and latency issues associated with exchanging data between threads. Third is the Load balancing problem, which is the distribution of work across multiple threads so that they all perform roughly the same amount of work. Lastly, scalability is the challenge of making efficient use of a larger number of threads when software is run on more-capable systems. For example, if a program is written to make good use of four processor cores, will it scale properly when run on a system with eight processor cores? Each of these issues must be handled carefully to maximize application performance.
Parallel programming problems generally fall into one of several well known patterns to help logically design programs. A few of the more common parallel programming patterns and their relationship to the aforementioned decompositions are shown in Table 2.
Table 2 Common Parallel Programming Patterns
In many cases, the best way to achieve parallel execution is to focus directly on the tasks themselves. In this case, the task-level parallelism pattern makes the most sense. In this pattern, the problem is decomposed into a set of tasks that operate independently. It is often necessary to remove dependencies between tasks or separate dependencies using replication. Problems that fit into this pattern include the so-called embarrassingly parallel problems, those where there are no dependencies between threads, and replicated data problems, those where the dependencies between threads may be removed from the individual threads.
In the divide and conquer pattern, the problem is divided into a number of parallel sub-problems. Each sub-problem is solved independently. Once each sub-problem is solved, the results are aggregated into the final solution. Since each sub-problem can be independently solved, these sub-problems may be executed in a parallel fashion.
The divide and conquer approach is widely used on sequential algorithms, such as merge sort. These algorithms are very easy to parallelize. This pattern typically does a good job of load balancing and exhibits good locality; which is important for effective cache usage.
The geometric decomposition pattern is based on the parallelization of the data structures used in the problem being solved. In geometric decomposition, each thread is responsible for operating on data "chunks." This pattern may be applied to problems such as heat flow and wave propagation.
The idea behind the pipeline pattern is identical to that of an assembly line. The way to find concurrency here is to break down the computation into a series of stages and have each thread work on a different stage simultaneously.
The wavefront pattern is useful when processing data elements along a diagonal in a two-dimensional grid. This is shown in Figure 1.
Figure 1 Wavefront Data Access Pattern
The numbers in Figure 1 illustrate the order in which the data elements are processed. For example, elements in the diagonal that contains the number "3" are dependent on data elements "1" and "2" being processed previously. The shaded data elements in Figure 1 indicate data that has already been processed. In this pattern, it is critical to minimize the idle time spent by each thread. Load balancing is the key to success with this pattern.
For more information about multi-thread and multi-core programming, please refer to the book Multi-Core Programming by Shameem Akhter and Jason Roberts.
About the Authors
Shameem Akhter is a platform architect at Intel, focusing on single socket multi-core architecture and performance analysis. He has also worked as a senior software engineer with the Intel Software and Solutions Group, designing application optimizations for desktop and server platforms. Shameem holds a patent on a threading interface for constraint programming, developed as a part of his master's thesis in computer science.
Jason Roberts is a senior software engineer at Intel Corporation. Over the past 10 years, Jason has worked on a number of different multi-threaded software products that span a wide range of applications targeting desktop, handheld, and embedded DSP platforms.
Copyright © 2009 Intel Corporation. All rights reserved.
This article is based on material found in book Multi-Core Programming by Shameem Akhter and Jason Roberts. Visit the Intel Press Web site to learn more about this book.
No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4744. Requests to the Publisher for permission should be addressed to the Publisher, Intel Press, Intel Corporation, 2111 NE 25 Avenue, JF3-330, Hillsboro, OR 97124-5961. E-mail: email@example.com.
May 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
May 22, 2013 |
At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
May 15, 2013 |
Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.