CSC/ECE 506 Spring 2011/ch2 JR

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Supplement to Chapter 2: The Data Parallel Programming Model

Chapter 2 of Solihin (2008) covers the shared memory and message passing parallel programming models. However, it does not address the data parallel model, another commonly recognized parallel programming model covered in other treatments like Foster (1995) and Culler (1999). It also does not give any historical context for how parallel programming models have evolved.

Shared memory and message passing models are often present as competing models, but the data parallel model addresses fundamentally different programming concerns and can therefore be used in conjunction with either. The goal of this supplement is to provide a treatment of the data parallel model which complements Chapter 2 of Solihin (2008). The task parallel model will also be introduced as a point of contrast.

Overview

Whereas the shared memory and message passing models focus on how parallel tasks access common data, the data parallel model focuses on how to divide up work into parallel tasks. Data parallel algorithms exploit parallelism by dividing a problem into a number of identical tasks which execute on different subsets of common data. The logical opposite of data parallel is task parallel, in which a number of distinct tasks operate on common data. Historically, each parallel programming model developed to exploit performance gains made possible by advancements in computer architecture.

History

As computer architectures have evolved, so have parallel programming models. The earliest advancements in parallel computers took advantage of bit-level parallelism. These computers used vector processing, which required a shared memory programming model. As performance returns from this architecture diminished, the emphasis was placed on instruction-level parallelism and the message passing model began to dominate. Most recently, with the move to cluster-based machines, there has been an increased emphasis on thread-level parallelism. This has corresponded to an increase interest in the data parallel programming model.

Bit-level parallelism in the 1970's

The major performance improvements from computers during this time were due to the ability to execute 32-bit word size operations at one time (Culler (1999), p. 15.). The dominant supercomputers of the time, like the Cray and the ILLIAC IV, were mainly Single Instruction Multiple Data architectures and used a shared memory programming model. They each used different forms of vector processing (Culler (1999), p. 21.). Development of the ILLIAC IV began in 1964 and wasn't finished until 1975 [1]. A central processor was connected to the main memory and delegated tasks to individual PE's, which each had their own memory cache. [2]. Each PE could operate either an 8-, 32- or 64-bit operand at a given time [3].

The Cray machine was installed at Los Alamos National Laborartory in1976 by Control Data Corporation and had similar performance to the ILLIAC IV [4]. The Cray machine relied heavily on the use of registers instead of individual processors like the ILLIAC IV. Each processor was connected to main memory and had a number of 64-bit registers used to perform operations [5].

Move to instruction-level parallelism in the 1980's

Increasing the word size above 32-bits offered diminishing returns in terms of performance (Culler (1999), p. 15.). In the mid-1980's the emphasis changed from bit-level parallelism to instruction-level parallelism, which involved increasing the number of instructions that could be executed at one time (Culler (1999), p. 15.). The message passing model allowed programmers the ability to divide up instructions in order to take advantage of this architecture.

Thread-level parallelism

The move to cluster-based machines in the past decade, has added another layer of complexity to parallelism. Since computers could be located across a network from each other, there is more emphasis on software acting as a bridge [6]. This has led to a greater emphasis on thread- or task-level parallelism [7] and the addition of the data parallelism programming model to existing message passing or shared memory models [8].

Data Parallel Model

One important feature of data-parallel programming model or data parallelism (SIMD) is the single control flow. Flynn's taxonomy classifies SIMD to be analogous to doing the same operation repeatedly over a large data set. There is only one control processor that directs the activities of all the processing elements. In a multiprocessor system executing a single set of instructions (SIMD), data parallelism is achieved when each processor performs the same task on different pieces of distributed data. In some situations, a single execution thread controls operations on all pieces of data. In others, different threads control the operation, but they execute the same code.

Description and Example

This section shows a simple example adapted from Solihin textbook (pp. 24 - 27) that illustrates the data-parallel programming model. Each of the codes below are written in pseudo-code style.

Suppose we want to perform the following task on an array a: updating each element of a by the product of itself and its index, and adding together the elements of a into the variable sum. The corresponding code is shown below. 

// simple sequential task
sum = 0;
for (i = 0; i < a.length; i++)
{
   a[i] = a[i] * i;
   sum = sum + a[i];
}

When we orchestrate the task using the data-parallel programming model, the program can be divided into two parts. The first part performs the same operations on separate elements of the array for each processing element (sometimes referred to as PE or pe), and the second part reorganizes data among all processing elements (In our example data reorganization is summing up values across different processing elements). Since data-parallel programming model only defines the overall effects of parallel steps, the second part can be accomplished either through shared memory or message passing. The three code fragments below are examples for the first part of the program, shared-memory version of the second part, and message passing for the second part, respectively. 


// data parallel programming: let each PE perform the same task on different pieces of distributed data
pe_id = getid();
my_sum = 0;
for (i = pe_id; i < a.length; i += number_of_pe)         //separate elements of the array are assigned to each PE 
{
   a[i] = a[i] * i;
   my_sum = my_sum + a[i];                               //all PEs accumulate elements assigned to them into local variable my_sum
}


In the above code, data parallelism is achieved by letting each processing element perform actions on array's separate elements, which are identified using the PE's id. For instance, if three processing elements are used then one processing element would start at i = 0, one would start at i = 1, and the last would start at i = 2. Since there are three processing elements then the index of the array for each will increase by three on each iteration until the task is complete (note that in our example elements assigned to each PE are interleaved instead of continuous). If the length of the array is a multiple of three then each processing element takes the same amount of time to execute its portion of the task.


The picture below illustrates how elements of the array are assigned among different PEs for the specific case: length of the array is 7 and there are 3 PEs available. Elements in the array are marked by their indexes (0 to 6). As shown in the picture, PE0 will work on elements with index 0, 3, 6; PE1 is in charge of elements with index 1, 4; and elements with index 2, 5 are assigned to PE2. In this way, these 3 PEs work collectively on the array, while each PE works on different elements. Thus, data parallelism is achieved.

Illustration of data parallel programming(adapted from Introduction to Parallel Computing)

Comparison with Message Passing and Shared Memory

Although the shared memory and message passing models may be combined into hybrid approaches, the two models are fundamentally different ways of addressing the same problem (of access control to common data). In contrast, the data parallel model is concerned with a fundamentally different problem (how to divide work into parallel tasks). As such, the data parallel model may be used in conjunction with either the shared memory or the message passing model without conflict. In fact, Klaiber (1994) compares the performance of a number of data parallel programs implemented with both shared memory and message passing models. One of the major advantages of combining the data parallel and message passing models is a reduction in the amount and complexity of communication required relative to a task parallel approach. Similarly, combining the data parallel and shared memory models tends to simplify and reduce the amount of synchronization required. If the task parallel code given above were modified from a message passing model to a shared memory model, the two threads would require 8 signals be sent between the threads (instead of 8 messages). In contrast, the data parallel code would require a single barrier before the local sums are added to compute the full sum. Much as the shared memory model can benefit from specialized hardware, the data parallel programming model can as well. SIMD (single-instruction-multiple-data) processors are specifically designed to run data parallel algorithms. These processors perform a single instruction on many different data locations simultaneously. Modern examples include CUDA processors developed by nVidia and Cell processors developed by STI (Sony, Toshiba, and IBM). For the curious, example code for CUDA processors is provided in the Appendix. However, whereas the shared memory model can be a difficult and costly abstraction in the absence of hardware support, the data parallel model—like the message passing model—does not require hardware support. Since data parallel code tends to simplify communication and synchronization, data parallel code may be easier to develop than a more task parallel approach. However, data parallel code also requires writing code to split program data into chunks and assign it to different threads. In addition, it is possible that a problem may not decompose easily into subproblems relying on largely independent chunks of data. In this case, it may be impractical or impossible to apply the data parallel model. Once written, data parallel programs can scale easily to large numbers of processors. The data parallel model implicitly encourages data locality by having each thread work on a chunk of data. The regular data chunks also make it easier to reason about where to locate data and how to organize it.

Task Parallel Model

Task Parallelism is a form of parallelization where multiple instructions are executed either on same data or multiple data. It focuses on distributing execution of processes(threads) across different parallel computing nodes. As a part of workflow, different execution threads communicate with one another as they work to share data.

Description and Example

If the task to be accomplished is to compute the sum of the results associated with the execution of instruction 'A' and instructions 'B'. The following example illustrates, how task parallelism can be achieved.

The pseudo code below illustrates task parallelism: 

program:
do 
...

if CPU="a" then
   do task "A"
else if CPU="b" then
   do task "B"
end if

end program

If we write the code as above and launch it on a 2-processor system, then the runtime environment will execute it accordingly. In an SPMD system, both CPUs will execute the code. In a parallel environment, both will have access to the same data. The "if" clause differentiates between the CPU's. CPU "a" will read true on the "if" and CPU "b" will read true on the "else if", thus having their own task. Now, both CPU's execute separate code blocks simultaneously, performing different tasks simultaneously. Code executed by CPU "a": program: ... do task "A" ... end program Code executed by CPU "b": program: ... do task "B" ... end program This concept can now be generalized to any number of processors. 


 
program:
...
if CPU="a" then
   do task "A"
else if CPU="b" then
   do task "B"

...

if CPU ="n" then
   do task "N"

end if
...
end program

Data Parallel Model vs Task Parallel Model

One important feature of data-parallel programming model or data parallelism (SIMD) is the single control flow: there is only one control processor that directs the activities of all the processing elements. In stark contrast to this is task parallelism (MIMD: Multiple Instruction, Multiple Data): characterized by its multiple control flows, it allows the concurrent execution of multiple instruction streams, each manipulates its own data and services separate functions. Below is a contrast between the data parallelism and task parallelism models from wikipedia: SIMD and MIMD. In the following subsections we continue to compare and contrast different features of data-parallel model and task-parallel model to help reader understand the unique characteristics of data-parallel programming model.

contrast between data parallelism and task parallelism

Synchronous vs Asynchronous

While the lockstep imposed by data parallelism on all data streams ensures synchronous computation (all PEs perform their tasks at the exact same pace), every processor in task parallelism performs its task at their own pace, which we call asynchronous computation. Thus, at a certain point of a task parallel program's execution, communication and synchronization primitives are needed to allow different instruction streams to coordinate their efforts, and that is where variable-sharing and message-passing come into play.


Determinism vs. Non-Determinism

Data parallelism's synchronous nature and task parallelism's asynchronism give rise to another pair of features that add to the difference between these two models: determinism versus non-determinism. Data parallelism is deterministic, i.e. computing with the same input will always yield the same result, since its synchronism ensures that issues like relative timing between PEs will not arise. In contrast, task parallelism's asynchronous updates of common data can give rise to non-determinism, i.e, the same input won't always yield the same computation result (the result of a computation will depend also on factors outside the program control, such as scheduling and timing of other PEs). Obviously, non-determinism makes it harder to write and maintain correct programs. This partially explains the advantage of data parallel programming model over data parallelism in terms of development effort (also discussed in section 4.2).


Major differences between data parallel and task parallel models can broadly be classified as the following

Comparison between data parallel and task parallel programming models.
Aspects Data Parallel Task Parallel
Decomposition Partition data into subsets Partition program into subtasks
Parallel tasks Identical Unique
Degree of parallelism Scales easily Fixed
Load balancing Easier Harder
Communication overhead Lower Higher

Definitions

  • Data parallel. A data parallel algorithm is composed of a set of identical tasks which operate on different subsets of common data.
  • Task parallel. A task parallel algorithm is composed of a set of differing tasks which operate on common data.
  • SIMD (single-instruction-multiple-data). A processor which executes a single instruction simultaneously on multiple data locations.
  • MIMD (multiple-instruction-multiple-data). A processor architecture which can execute multiple instruction across multiple data elements simultaneously.

References

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