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Introduction to Parallel Computing
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Table of Contents
- Abstract
- Overview
- What is Parallel Computing?
- Why Use Parallel Computing?
- Concepts and Terminology
- von Neumann Computer Architecture
- Flynn's Classical Taxonomy
- Some General Parallel Terminology
- Parallel Computer Memory Architectures
- Shared Memory
- Distributed Memory
- Hybrid Distributed-Shared Memory
- Parallel Programming Models
- Overview
- Shared Memory Model
- Threads Model
- Message Passing Model
- Data Parallel Model
- Other Models
- Designing Parallel Programs
- Automatic vs. Manual Parallelization
- Understand the Problem and the Program
- Partitioning
- Communications
- Synchronization
- Data Dependencies
- Load Balancing
- Granularity
- I/O
- Limits and Costs of Parallel Programming
- Performance Analysis and Tuning
- Parallel Examples
- Array Processing
- PI Calculation
- Simple Heat Equation
- 1-D Wave Equation
- References and More Information
This presentation covers the basics of parallel computing. Beginning with a
brief overview and some concepts and terminology associated with parallel
computing, the topics of parallel memory architectures and programming models
are then explored. These topics are followed by a discussion on a number of
issues related to designing parallel programs. The last portion of the
presentation is spent examining how to parallelize several different types
of serial programs.
Level/Prerequisites: None
What is Parallel Computing?
- Traditionally, software has been written for serial
computation:
- To be run on a single computer having a single Central Processing
Unit (CPU);
- A problem is broken into a discrete series of instructions.
- Instructions are executed one after another.
- Only one instruction may execute at any moment in time.
- In the simplest sense, parallel computing is the simultaneous
use of multiple compute resources to solve a computational problem.
- To be run using multiple CPUs
- A problem is broken into discrete parts that can be solved concurrently
- Each part is further broken down to a series of instructions
- Instructions from each part execute simultaneously on different CPUs
- The compute resources can include:
- A single computer with multiple processors;
- An arbitrary number of computers connected by a network;
- A combination of both.
- The computational problem usually demonstrates characteristics such as
the ability to be:
- Broken apart into discrete pieces of work that can be solved
simultaneously;
- Execute multiple program instructions at any moment in time;
- Solved in less time with multiple compute resources than with a single
compute resource.
- Parallel computing is an evolution of serial computing that
attempts to emulate what has always been the state of affairs in the natural
world: many complex, interrelated events happening at the same time, yet
within a sequence. Some examples:
- Planetary and galactic orbits
- Weather and ocean patterns
- Tectonic plate drift
- Rush hour traffic in LA
- Automobile assembly line
- Daily operations within a business
- Building a shopping mall
- Ordering a hamburger at the drive through.
- Traditionally, parallel computing has been considered to be
"the high end of computing" and has been motivated by numerical
simulations of complex systems and "Grand Challenge Problems" such as:
- weather and climate
- chemical and nuclear reactions
- biological, human genome
- geological, seismic activity
- mechanical devices - from prosthetics to spacecraft
- electronic circuits
- manufacturing processes
- Today, commercial applications are providing an equal or greater driving
force in the development of faster computers.
These applications require the processing of large
amounts of data in sophisticated ways. Example applications include:
- parallel databases, data mining
- oil exploration
- web search engines, web based business services
- computer-aided diagnosis in medicine
- management of national and multi-national corporations
- advanced graphics and virtual reality, particularly in the entertainment
industry
- networked video and multi-media technologies
- collaborative work environments
- Ultimately, parallel computing is an attempt to maximize the infinite but
seemingly scarce commodity called time.
Why Use Parallel Computing?
- The primary reasons for using parallel computing:
- Save time - wall clock time
- Solve larger problems
- Provide concurrency (do multiple things at the same time)
- Other reasons might include:
- Taking advantage of non-local resources - using available compute
resources on a wide area network, or even the Internet when local
compute resources are scarce.
- Cost savings - using multiple "cheap" computing resources instead
of paying for time on a supercomputer.
- Overcoming memory constraints - single computers have very finite
memory resources. For large problems, using the memories
of multiple computers may overcome this obstacle.
- Limits to serial computing - both physical and practical reasons pose
significant constraints to simply building ever faster serial computers:
- Transmission speeds - the speed of a serial computer is directly
dependent upon how fast data can move through hardware.
Absolute limits are the speed of light (30 cm/nanosecond) and the
transmission limit of copper wire (9 cm/nanosecond). Increasing
speeds necessitate increasing proximity of processing elements.
- Limits to miniaturization - processor technology is allowing an
increasing number of transistors to be placed on a chip. However,
even with molecular
or atomic-level components, a limit will be reached on how small
components can be.
- Economic limitations - it is increasingly expensive to make a single
processor faster. Using a larger number of moderately fast
commodity processors to
achieve the same (or better) performance is less expensive.
- The future: during the past 10 years, the trends indicated by ever faster
networks, distributed systems, and multi-processor computer architectures
(even at the desktop level) suggest that parallelism is the future
of computing.
von Neumann Architecture
- For over 40 years, virtually all computers have followed a common machine
model known as the von Neumann computer. Named after the Hungarian
mathematician John von Neumann.
- A von Neumann computer uses the stored-program concept.
The CPU executes a stored program that specifies
a sequence of read and write operations on the memory.
- Basic design:
- Memory is used to store both program and data instructions
- Program instructions are coded data which tell the computer to
do something
- Data is simply information to be used by the program
- A central processing unit (CPU) gets instructions and/or data from
memory, decodes the instructions and then sequentially
performs them.
Flynn's Classical Taxonomy
- There are different ways to classify parallel computers. One of the more
widely used classifications, in use since 1966, is called Flynn's Taxonomy.
- Flynn's taxonomy distinguishes multi-processor computer architectures
according
to how they can be classified along the two independent dimensions of
Instruction and Data. Each of these dimensions
can have only one of two possible states: Single or
Multiple.
- The matrix below defines the 4 possible classifications according to Flynn.
S I S D
Single Instruction, Single Data |
S I M D
Single Instruction, Multiple Data |
M I S D
Multiple Instruction, Single Data |
M I M D
Multiple Instruction, Multiple Data |
Single Instruction, Single Data (SISD):
- A serial (non-parallel) computer
- Single instruction: only one instruction stream is
being acted on by the CPU during any one clock cycle
- Single data: only one data stream is being used as input during any one clock cycle
- Deterministic execution
- This is the oldest and until recently, the most prevalent form of computer
- Examples: most PCs, single CPU workstations and mainframes
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Single Instruction, Multiple Data (SIMD):
- A type of parallel computer
- Single instruction: All processing units execute the same instruction at any given clock cycle
- Multiple data: Each processing unit can operate on a different data element
- This type of machine typically has an instruction dispatcher, a very
high-bandwidth internal network, and a very large array of very
small-capacity instruction units.
- Best suited for specialized problems characterized by a high degree of
regularity,such as image processing.
- Synchronous (lockstep) and deterministic execution
- Two varieties: Processor Arrays and Vector Pipelines
- Examples:
- Processor Arrays: Connection Machine CM-2, Maspar MP-1, MP-2
- Vector Pipelines: IBM 9000, Cray C90, Fujitsu VP, NEC SX-2,
Hitachi S820
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Multiple Instruction, Single Data (MISD):
- A single data stream is fed into multiple processing units.
- Each processing unit operates on the data independently via independent
instruction streams.
- Few actual examples of this class of parallel computer have ever existed.
One is the experimental Carnegie-Mellon C.mmp computer (1971).
- Some conceivable uses might be:
- multiple frequency filters operating on a single signal stream
- multiple cryptography algorithms attempting to crack a single coded
message.
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Multiple Instruction, Multiple Data (MIMD):
- Currently, the most common type of parallel computer. Most modern
computers fall into this category.
- Multiple Instruction: every processor may be executing a different
instruction stream
- Multiple Data: every processor may be working with a different data
stream
- Execution can be synchronous or asynchronous, deterministic or
non-deterministic
- Examples: most current supercomputers, networked parallel computer
"grids" and multi-processor SMP computers - including some types of PCs.
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Some General Parallel Terminology
Like everything else, parallel computing has its own "jargon". Some of the
more commonly used terms associated with parallel computing are listed below.
Most of these will be discussed in more detail later.
- Task
- A logically discrete section of computational work. A task is typically a
program or program-like set of instructions that is executed by a processor.
- Parallel Task
- A task that can be executed by multiple processors safely (yields correct
results)
- Serial Execution
- Execution of a program sequentially, one statement at a time. In the
simplest sense, this is what happens on a one processor machine. However,
virtually all parallel tasks will have sections of a parallel program that
must be executed serially.
- Parallel Execution
- Execution of a program by more than one task, with each task being able to
execute the same or different statement at the same moment in time.
- Shared Memory
- From a strictly hardware point of view, describes a computer architecture
where all processors have direct (usually bus based) access to common
physical memory. In a programming sense, it describes a model where
parallel tasks all have the same "picture" of memory and can directly
address and access the same logical memory locations regardless
of where the physical memory actually exists.
- Distributed Memory
- In hardware, refers to network based memory access for physical memory that
is not common. As a programming model, tasks can only logically "see"
local machine memory and must use communications to access memory on other
machines where other tasks are executing.
- Communications
- Parallel tasks typically need to exchange data. There are several ways this can be
accomplished, such as through a shared memory bus or over a network, however the actual
event of data exchange is commonly referred to as communications regardless of the method
employed.
- Synchronization
- The coordination of parallel tasks in real time, very often associated with
communications. Often implemented by establishing a synchronization point within an
application where a task
may not proceed further until another task(s) reaches the same or logically equivalent
point.
Synchronization usually involves waiting by at least one task, and can therefore cause
a parallel application's wall clock execution time to increase.
- Granularity
- In parallel computing, granularity is a qualitative measure of the ratio
of computation to communication.
- Coarse: relatively large amounts of computational work
are done between communication events
- Fine: relatively small amounts of computational work are
done between communication events
- Observed Speedup
- Observed speedup of a code which has been parallelized, defined as:
wall-clock time of serial execution
wall-clock time of parallel execution
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One of the simplest and most widely used indicators for a parallel program's performance.
- Parallel Overhead
- The amount of time required to coordinate parallel tasks, as opposed to
doing useful work. Parallel overhead can include factors such as:
- Task start-up time
- Synchronizations
- Data communications
- Software overhead imposed by parallel compilers, libraries, tools,
operating system, etc.
- Task termination time
- Massively Parallel
- Refers to the hardware that comprises a given parallel system - having many processors.
The meaning of many keeps increasing, but currently BG/L pushes this number
to 6 digits.
- Scalability
- Refers to a parallel system's (hardware and/or software) ability to demonstrate
a proportionate increase in parallel speedup with the addition of more processors.
Factors that contribute to scalability include:
- Hardware - particularly memory-cpu bandwidths and network communications
- Application algorithm
- Parallel overhead related
- Characteristics of your specific application and coding
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Parallel Computer Memory Architectures |
Shared Memory
General Characteristics:
- Shared memory parallel computers vary widely, but generally have in common
the ability for all processors to access all memory as global address space.
- Multiple processors can operate independently but share the same memory
resources.
- Changes in a memory location effected by one processor are visible to all
other processors.
- Shared memory machines can be divided into two main classes based upon
memory access times: UMA and NUMA.
Uniform Memory Access (UMA):
- Most commonly represented today by Symmetric Multiprocessor (SMP)
machines
- Identical processors
- Equal access and access times to memory
- Sometimes called CC-UMA - Cache Coherent UMA.
Cache coherent means if one processor updates a location in shared
memory, all
the other processors know about the update. Cache coherency is
accomplished at the hardware level.
Non-Uniform Memory Access (NUMA):
- Often made by physically linking two or more SMPs
- One SMP can directly access memory of another SMP
- Not all processors have equal access time to all memories
- Memory access across link is slower
- If cache coherency is maintained, then may also be called CC-NUMA -
Cache Coherent NUMA
Advantages:
- Global address space provides a user-friendly programming perspective
to memory
- Data sharing between tasks is both fast and uniform due to the proximity
of memory to CPUs
Disadvantages:
- Primary disadvantage is the lack of scalability between memory and CPUs.
Adding more CPUs can geometrically increases traffic on the shared
memory-CPU path, and for cache coherent systems, geometrically increase
traffic associated with cache/memory management.
- Programmer responsibility for synchronization constructs that insure
"correct" access of global memory.
- Expense: it becomes increasingly difficult and expensive to design and
produce shared memory machines with ever increasing numbers of
processors.
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Parallel Computer Memory Architectures |
Distributed Memory
General Characteristics:
- Like shared memory systems, distributed memory systems vary widely but
share a common characteristic. Distributed memory systems require a
communication network to connect inter-processor memory.
- Processors have their own local memory. Memory addresses in one
processor do not map to another processor, so there is no concept of
global address space across all processors.
- Because each processor has its own local memory, it operates
independently. Changes it makes to its local memory have no effect
on the memory of other processors. Hence, the concept of cache
coherency does not apply.
- When a processor needs access to data in another processor, it is
usually the task of the programmer to explicitly define how and when
data is communicated. Synchronization between tasks is likewise the
programmer's responsibility.
- The network "fabric" used for data transfer varies widely, though it can
can be as simple as Ethernet.
Advantages:
- Memory is scalable with number of processors. Increase the number of
processors and the size of memory increases proportionately.
- Each processor can rapidly access its own memory without interference
and without the overhead incurred with trying to maintain cache
coherency.
- Cost effectiveness: can use commodity, off-the-shelf processors and
networking.
Disadvantages:
- The programmer is responsible for many of the details associated with
data communication between processors.
- It may be difficult to map existing data structures, based on global
memory, to this memory organization.
- Non-uniform memory access (NUMA) times
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Parallel Computer Memory Architectures |
Hybrid Distributed-Shared Memory
- Summarizing a few of the key characteristics of
shared and distributed memory machines:
| Comparison of Shared and Distributed Memory Architectures |
| Architecture |
CC-UMA |
CC-NUMA |
Distributed |
| Examples |
SMPs
Sun Vexx
DEC/Compaq
SGI Challenge
IBM POWER3 |
SGI Origin
Sequent
HP Exemplar
DEC/Compaq
IBM POWER4 (MCM) |
Cray T3E
Maspar
IBM SP2 |
| Communications |
MPI
Threads
OpenMP
shmem |
MPI
Threads
OpenMP
shmem |
MPI |
| Scalability |
to 10s of processors |
to 100s of processors |
to 1000s of processors |
| Draw Backs |
Memory-CPU bandwidth |
Memory-CPU bandwidth
Non-uniform access times |
System administration
Programming is hard to develop and maintain |
| Software Availability |
many 1000s ISVs |
many 1000s ISVs |
100s ISVs |
- The largest and fastest computers in the world today employ both shared
and distributed memory architectures.
- The shared memory component is usually a cache coherent SMP machine.
Processors on a given SMP can address that machine's memory as global.
- The distributed memory component is the networking of multiple SMPs.
SMPs know only about their own memory - not the memory on another SMP.
Therefore, network communications are required to move data from one
SMP to another.
- Current trends seem to indicate that this type of memory architecture
will continue to prevail and increase at the high end of computing for
the foreseeable future.
- Advantages and Disadvantages: whatever is common to both shared and
distributed memory architectures.
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Parallel Programming Models |
Overview
- There are several parallel programming models in common use:
- Shared Memory
- Threads
- Message Passing
- Data Parallel
- Hybrid
- Parallel programming models exist as an abstraction above hardware
and memory architectures.
- Although it might not seem apparent, these models are NOT specific
to a particular type of machine or memory architecture. In fact, any
of these models can (theoretically) be implemented on any underlying
hardware. Two examples:
- Shared memory model on a distributed memory machine:
Kendall Square Research (KSR) ALLCACHE approach.
Machine memory was physically
distributed, but appeared to the user as a single shared memory
(global address space). Generically, this approach is referred to as
"virtual shared memory". Note: although KSR is no longer in business,
there is no reason to suggest that a similar implementation will not
be made available by another vendor in the future.
- Message passing model on a shared memory machine: MPI on SGI Origin.
The SGI Origin employed the CC-NUMA type of shared memory architecture,
where every task has direct access to global memory. However, the
ability to
send and receive messages with MPI, as is commonly done over a network
of distributed memory machines, is not only implemented but is very
commonly used.
- Which model to use is often a combination of what is available and personal
choice. There is no "best" model, although there certainly are better
implementations of some models over others.
- The following sections describe each of the models mentioned above, and
also discuss some of their actual implementations.
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Parallel Programming Models |
Shared Memory Model
- In the shared-memory programming model, tasks share a common address space,
which they read and write asynchronously.
- Various mechanisms such as locks / semaphores may be used to control
access to the shared memory.
- An advantage of this model from the programmer's point of view is that the
notion of data "ownership" is lacking, so there is no need to specify
explicitly the communication of data between tasks. Program
development can often be simplified.
- An important disadvantage in terms of performance is that it becomes
more difficult to understand and manage data locality.
Implementations:
- On shared memory platforms, the native compilers translate
user program variables into actual memory addresses, which are global.
- No common distributed memory platform implementations currently exist.
However, as mentioned previously in the Overview section, the KSR
ALLCACHE approach provided a shared memory view of data even though
the physical memory of the machine was distributed.
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Parallel Programming Models |
Threads Model
- In the threads model of parallel programming, a single process can have
multiple, concurrent execution paths.
- Perhaps the most simple analogy that can be used to describe threads is the
concept of a single program that includes a number of subroutines:
- The main program a.out is scheduled to run by the
native operating system. a.out loads and acquires all of the
necessary system and user resources to run.
- a.out performs some serial work, and then creates
a number of tasks (threads) that can be scheduled and run by the
operating system concurrently.
- Each thread has local data, but also, shares the entire resources of
a.out. This saves the overhead associated with
replicating a program's resources for each thread. Each thread also
benefits from a global memory view because it shares the memory space
of a.out.
- A thread's work may best be described as a subroutine within
the main program. Any thread can execute any subroutine at the
same time as other threads.
- Threads communicate with each other through global memory (updating
address locations). This requires synchronization constructs to insure
that more than one thread is not updating the same global address at
any time.
- Threads can come and go, but a.out remains present
to provide the necessary shared resources until the
application has completed.
- Threads are commonly associated with shared memory architectures and
operating systems.
Implementations:
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Parallel Programming Models |
Message Passing Model
- The message passing model demonstrates the following characteristics:
- A set of tasks that use their own local memory during computation.
Multiple tasks can reside on the same physical machine as well
across an arbitrary number of machines.
- Tasks exchange data through communications by sending and
receiving messages.
- Data transfer usually requires cooperative operations to be performed
by each process. For example, a send operation must have a matching
receive operation.
Implementations:
- From a programming perspective, message passing implementations commonly
comprise a library of subroutines that are imbedded in source code.
The programmer is responsible for determining all parallelism.
- Historically, a variety of message passing libraries have been
available since the 1980s. These implementations differed substantially
from each other making it difficult for programmers to develop portable
applications.
- In 1992, the MPI Forum was formed with the primary goal of establishing
a standard interface for message passing implementations.
- Part 1 of the Message Passing Interface (MPI) was released in
1994. Part 2 (MPI-2) was released in 1996.
Both MPI specifications are available on the web at
www.mcs.anl.gov/Projects/mpi/standard.html.
- MPI is now the "de facto" industry
standard for message passing, replacing virtually all other
message passing implementations used for production work.
Most, if not all of the popular parallel computing platforms
offer at least one implementation of MPI. A few offer a full
implementation of MPI-2.
- For shared memory architectures, MPI implementations usually don't
use a network for task communications. Instead, they use shared
memory (memory copies) for performance reasons.
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Parallel Programming Models |
Data Parallel Model
- The data parallel model demonstrates the following characteristics:
- Most of the parallel work focuses on performing operations on a
data set. The data set is typically organized into a common
structure, such as an array or cube.
- A set of tasks work collectively on the same data structure, however,
each task works on a different partition of the same data structure.
- Tasks perform the same operation on their partition of work, for
example, "add 4 to every array element".
- On shared memory architectures, all tasks may have access to the data
structure through global memory. On distributed memory architectures
the data structure is split up and resides as "chunks" in the local
memory of each task.
Implementations:
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Parallel Programming Models |
Other Models
- Other parallel programming models besides those previously mentioned
certainly exist, and will continue to evolve along with the ever
changing world of computer hardware and software. Only three of
the more common ones are mentioned here.
Hybrid:
- In this model, any two or more parallel programming models
are combined.
- Currently, a common example of a hybrid model is the combination
of the message passing model (MPI) with either the threads model
(POSIX threads) or the shared memory model (OpenMP). This hybrid
model lends itself well to the increasingly common hardware
environment of networked SMP machines.
- Another common example of a hybrid model is combining data
parallel with message passing. As mentioned in the
data parallel model section previously, data parallel
implementations (F90, HPF) on distributed memory architectures
actually use message passing to transmit data between tasks,
transparently to the programmer.
Single Program Multiple Data (SPMD):
- SPMD is actually a "high level" programming model that can be
built upon any combination of the previously mentioned parallel
programming models.
- A single program is executed by all tasks simultaneously.
- At any moment in time, tasks can be executing the same or different
instructions within the same program.
- SPMD programs usually have the necessary logic programmed into them to
allow different tasks to branch or conditionally execute only those
parts of the program they are designed to execute. That is, tasks
do not necessarily have to execute the entire program - perhaps only a
portion of it.
- All tasks may use different data
Multiple Program Multiple Data (MPMD):
- Like SPMD, MPMD is actually a "high level" programming model that can
be built upon any combination of the previously mentioned parallel
programming models.
- MPMD applications typically have multiple executable object files
(programs). While the application is being run in parallel, each
task can be executing
the same or different program as other tasks.
- All tasks may use different data
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Designing Parallel Programs |
Automatic vs. Manual Parallelization
- Designing and developing parallel programs has characteristically been a
very manual process. The programmer is typically responsible for
both identifying and actually implementing parallelism.
- Very often, manually developing parallel codes is a time consuming,
complex, error-prone and iterative process.
- For a number of years now, various tools have been available to assist
the programmer with converting serial programs into parallel programs.
The most common type of tool used to automatically parallelize a serial
program is a parallelizing compiler or pre-processor.
- A parallelizing compiler generally works in two different ways:
- Fully Automatic
- The compiler analyzes the source code and
identifies opportunities for parallelism.
- The analysis includes
identifying inhibitors to parallelism and possibly a cost
weighting on whether or not the parallelism would actually
improve performance.
- Loops (do, for) loops are the most frequent target for
automatic parallelization.
- Programmer Directed
- Using "compiler directives" or possibly compiler flags,
the programmer explicitly tells the compiler how to
parallelize the code.
- May be able to be used in conjunction with some degree of
automatic parallelization also.
- If you are beginning with an existing serial code and have time
or budget constraints, then automatic parallelization may be
the answer. However, there are several important caveats that
apply to automatic parallelization:
- Wrong results may be produced
- Performance may actually degrade
- Much less flexible than manual parallelization
- Limited to a subset (mostly loops) of code
- May actually not parallelize code if the analysis suggests there
are inhibitors or the code is too complex
- Most automatic parallelization tools are for Fortran
- The remainder of this section applies to the manual method of
developing parallel codes.
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Designing Parallel Programs |
Understand the Problem and the Program
- Undoubtedly, the first step in developing parallel software is to
first understand the problem that you wish to solve in parallel.
If you are starting with a serial program, this necessitates
understanding the existing code also.
- Before spending time in an attempt to develop a parallel solution
for a problem, determine whether or not the problem is one that can
actually be parallelized.
- Example of Parallelizable Problem:
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Calculate the potential energy for each of several thousand
independent conformations of a molecule.
When done, find the minimum energy conformation.
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This problem is able to be solved in parallel. Each of the
molecular conformations is independently determinable.
The calculation of the minimum energy conformation is also a
parallelizable problem.
- Example of a Non-parallelizable Problem:
Calculation of the Fibonacci series (1,1,2,3,5,8,13,21,...) by use of
the formula:
F(k + 2) = F(k + 1) + F(k)
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This is a non-parallelizable problem because the calculation of the
Fibonacci sequence as shown would entail
dependent calculations rather than independent ones.
The calculation of the k + 2 value uses those of
both k + 1 and k. These three terms cannot be calculated
independently and therefore, not in parallel.
- Identify the program's hotspots:
- Know where most of the real work is being done.
The majority of scientific and technical programs usually
accomplish most of their work in a few places.
- Profilers and performance analysis tools can help here
- Focus on parallelizing the hotspots and ignore those sections
of the program that account for little CPU usage.
- Identify bottlenecks in the program
- Are there areas that are disproportionately slow, or cause
parallelizable work to halt or be deferred?
For example, I/O is usually something that slows a program down.
- May be possible to restructure the program or use a different
algorithm to reduce or eliminate unnecessary slow areas
- Identify inhibitors to parallelism. One common class of inhibitor
is data dependence, as demonstrated by the Fibonacci sequence
above.
- Investigate other algorithms if possible. This may be the single most
important consideration when designing a parallel application.
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Designing Parallel Programs |
Partitioning
- One of the first steps in designing a parallel program is to break the
problem into discrete "chunks" of work that can be distributed to
multiple tasks. This is known as decomposition or partitioning.
- There are two basic ways to partition computational work among parallel
tasks: domain decomposition and
functional decomposition.
Domain Decomposition:
- In this type of partitioning, the data associated with a problem
is decomposed. Each parallel task then works on a portion of
of the data.
- There are different ways to partition data:
Functional Decomposition:
- In this approach, the focus is on the computation that is to be
performed rather than on the data manipulated by the computation.
The problem is decomposed according to the work that must be done.
Each task then performs a portion of the overall work.
- Functional decomposition lends itself well to problems that can be
split into different tasks. For example:
- Ecosystem Modeling
- Each program calculates the population
of a given group, where each group's growth depends on that of its
neighbors. As time progresses, each process calculates
its current state, then exchanges information with the neighbor
populations. All tasks then progress to calculate the state at the
next time step.
- Signal Processing
- An audio signal data set is passed
through four distinct computational filters. Each filter is a
separate process. The first segment of data must pass through the
first filter before progressing to the second. When it does, the
second segment of data passes through the first filter. By the time
the fourth segment of data is in the first filter, all four
tasks are busy.
- Climate Modeling
- Each model component can be thought of as a separate task.
Arrows represent exchanges of data between components during
computation: the atmosphere model generates wind velocity data
that are used by the ocean model, the ocean model generates sea
surface temperature data that are used by the atmosphere model,
and so on.
- Combining these two types of problem decomposition is common and natural.
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Designing Parallel Programs |
Communications
Who Needs Communications?
Factors to Consider:
There are a number of important factors to consider when designing your
program's inter-task communications:
- Cost of communications
- Inter-task communication virtually always implies overhead.
- Machine cycles and resources that could be used for computation
are instead used to package and transmit data.
- Communications frequently require some type of synchronization
between tasks, which can result in tasks spending time "waiting"
instead of doing work.
- Competing communication traffic can saturate the available network
bandwidth, further aggravating performance problems.
- Latency vs. Bandwidth
- latency is the time it takes to send a minimal (0 byte)
message from point A to point B. Commonly expressed as microseconds.
- bandwidth is the amount of data that can be communicated
per unit of time. Commonly expressed as megabytes/sec.
- Sending many small messages can cause latency to dominate communication
overheads. Often it is more efficient to package small messages into a
larger message, thus increasing the effective communications bandwidth.
- Visibility of communications
- With the Message Passing Model, communications are explicit and
generally quite visible and under the control of the programmer.
- With the Data Parallel Model, communications often occur
transparently to the programmer, particularly on distributed
memory architectures. The programmer may not even be able to
know exactly how inter-task communications are being accomplished.
- Synchronous vs. asynchronous communications
- Synchronous communications require some type of "handshaking"
between tasks that are sharing data. This can be explicitly
structured in code by the programmer, or it may happen at a
lower level unknown to the programmer.
- Synchronous communications are often referred to as
blocking communications since other work must
wait until the communications have completed.
- Asynchronous communications allow tasks to transfer data independently
from one another. For example, task 1 can prepare and send a
message to task 2, and then immediately begin doing other work.
When task 2 actually receives the data doesn't matter.
- Asynchronous communications are often referred to as
non-blocking communications since other work can
be done while the communications are taking place.
- Interleaving computation with communication is the single greatest
benefit for using asynchronous communications.
- Scope of communications
- Knowing which tasks must communicate with each other is critical during
the design stage of a parallel code. Both of the two scopings
described below can be implemented synchronously or asynchronously.
- Point-to-point - involves two tasks with one task
acting as the sender/producer of data, and the other acting as
the receiver/consumer.
- Collective - involves data sharing between more than
two tasks, which are often specified as being members in a common
group, or collective. Some common variations (there are more):
- Efficiency of communications
- Very often, the programmer will have a choice with regard to
factors that can affect communications performance. Only a
few are mentioned here.
- Which implementation for a given model should be used? Using
the Message Passing Model as an
example, one MPI implementation may be faster on a given
hardware platform than another.
- What type of communication operations should be used? As
mentioned previously, asynchronous communication operations
can improve overall program performance.
- Network media - some platforms may offer more than one network
for communications. Which one is best?
- Overhead and Complexity
- Finally, realize that this is only a partial list of things to consider!!!
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Designing Parallel Programs |
Synchronization
Types of Synchronization:
- Barrier
- Usually implies that all tasks are involved
- Each task performs its work until it reaches the barrier. It then
stops, or "blocks".
- When the last task reaches the barrier, all tasks are synchronized.
- What happens from here varies. Often, a serial section of work must
be done. In other cases, the tasks are automatically released to
continue their work.
- Lock / semaphore
- Can involve any number of tasks
- Typically used to serialize (protect) access to global data
or a section of code. Only one task at a time may use (own) the
lock / semaphore / flag.
- The first task to acquire the lock "sets" it. This task can then
safely (serially) access the protected data or code.
- Other tasks can attempt to acquire the lock but must wait until the
task that owns the lock releases it.
- Can be blocking or non-blocking
- Synchronous communication operations
- Involves only those tasks executing a communication operation
- When a task performs a communication operation, some form of
coordination is required with the other task(s) participating in
the communication. For example, before a task can perform a
send operation, it must first receive an acknowledgment from the
receiving task that it is OK to send.
- Discussed previously in the Communications section.
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Designing Parallel Programs |
Data Dependencies
Definition:
- A dependence exists between program statements when
the order of statement execution affects the results of the program.
- A data dependence results from multiple use of the same
location(s) in storage by different tasks.
- Dependencies are important to parallel programming because they are one
of the primary inhibitors to parallelism.
Examples:
- Loop carried data dependence
DO 500 J = MYSTART,MYEND
A(J) = A(J-1) * 2.0
500 CONTINUE
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The value of A(J-1) must be computed before the value of A(J),
therefore A(J) exhibits a data dependency on A(J-1).
Parallelism is inhibited.
If Task 2 has A(J) and task 1 has A(J-1),
computing the correct value of A(J) necessitates:
- Distributed memory architecture - task 2 must obtain the value
of A(J-1) from task 1 after task 1 finishes its computation
- Shared memory architecture - task 2 must read A(J-1) after
task 1 updates it
- Loop independent data dependence
task 1 task 2
------ ------
X = 2 X = 4
. .
. .
Y = X**2 Y = X**3
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As with the previous example, parallelism is inhibited.
The value of Y is dependent on:
- Distributed memory architecture - if or when the value of X is
communicated between the tasks.
- Shared memory architecture - which task last stores the value of X.
- Although all data dependencies are important to identify when designing
parallel programs, loop carried dependencies are particularly important
since loops are possibly the most common target of parallelization efforts.
How to Handle Data Dependencies:
- Distributed memory architectures - communicate required data at
synchronization points.
- Shared memory architectures -synchronize read/write operations between
tasks.
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Designing Parallel Programs |
Load Balancing
- Load balancing refers to the practice of distributing work among tasks
so that all tasks are kept busy all of the time.
It can be considered a minimization of task idle time.
- Load balancing is important to parallel programs for performance
reasons. For example, if all tasks are subject to a barrier
synchronization point, the slowest task will determine the overall
performance.
How to Achieve Load Balance:
- Equally partition the work each task receives
- For array/matrix operations where each task performs similar
work, evenly distribute the data set among the tasks.
- For loop iterations where the work done in each iteration
is similar, evenly distribute the iterations across the tasks.
- If a heterogeneous mix of machines with varying performance
characteristics are being used, be sure to use some type of performance
analysis tool to detect any load imbalances. Adjust work accordingly.
- Use dynamic work assignment
- Certain classes of problems result in load imbalances even if data
is evenly distributed among tasks:
- Sparse arrays - some tasks will have actual data to work on
while others have mostly "zeros".
- Adaptive grid methods - some tasks may need to refine their
mesh while others don't.
- N-body simulations - where some particles may migrate
to/from their original task domain to another task's; where
the particles owned by some tasks require more work than
those owned by other tasks.
- When the amount of work each task will perform is intentionally
variable, or is unable to be predicted, it may be helpful to use
a scheduler - task pool approach. As each task finishes
its work, it queues to get a new piece of work.
- It may become necessary to design an algorithm which detects and handles
load imbalances as they occur dynamically within the code.
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Designing Parallel Programs |
Granularity
Computation / Communication Ratio:
- In parallel computing, granularity is a qualitative measure of the ratio
of computation to communication.
- Periods of computation are typically separated from periods of
communication by synchronization events.
Fine-grain Parallelism:
- Relatively small amounts of computational work are done between
communication events
- Low computation to communication ratio
- Facilitates load balancing
- Implies high communication overhead and less opportunity for
performance enhancement
- If granularity is too fine it is possible that the overhead
required for communications and synchronization between tasks
takes longer than the computation.
Coarse-grain Parallelism:
- Relatively large amounts of computational work are done between
communication/synchronization events
- High computation to communication ratio
- Implies more opportunity for performance increase
- Harder to load balance efficiently
Which is Best?
- The most efficient granularity is dependent on the algorithm and the
hardware environment in which it runs.
- In most cases the overhead associated with communications and
synchronization is high relative to execution speed
so it is advantageous to have coarse granularity.
- Fine-grain parallelism can help reduce overheads due to load imbalance.
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Designing Parallel Programs |
I/O
The Bad News:
- I/O operations are generally regarded as inhibitors to parallelism
- Parallel I/O systems are immature or not available for all platforms
- In an environment where all tasks see the same filespace, write
operations will result in file overwriting
- Read operations will be affected by the fileserver's ability to handle
multiple read requests at the same time
- I/O that must be conducted over the network (NFS, non-local) can cause
severe bottlenecks
The Good News:
- Some parallel file systems are available. For example:
- GPFS: General Parallel File System for AIX (IBM)
- Lustre: for Linux clusters (Cluster File Systems, Inc.)
- PVFS/PVFS2: Parallel Virtual File System for Linux clusters
(Clemson/Argonne/Ohio State/others)
- PanFS: Panasas ActiveScale File System for Linux clusters (Panasas,
Inc.)
- HP SFS: HP StorageWorks Scalable File Share. Lustre based parallel file
system (Global File System for Linux) product from HP
- The parallel I/O programming interface specification for MPI has been
available since 1996 as part of MPI-2. Vendor and "free" implementations
are now commonly available.
- Some options:
- If you have access to a parallel file system, investigate using
it. If you don't, keep reading...
- Rule #1: Reduce overall I/O as much as possible
- Confine I/O to specific serial portions of the job, and then use
parallel communications to distribute data to parallel tasks.
For example, Task 1 could read an input file and then communicate
required data to other tasks. Likewise, Task 1 could perform
write operation after receiving required data from all other tasks.
- For distributed memory systems with shared filespace, perform I/O in
local, non-shared filespace.
For example, each processor may have /tmp filespace which can used.
This is usually much more efficient than performing I/O over the
network to one's home directory.
- Create unique filenames for each tasks' input/output file(s)
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Designing Parallel Programs |
Limits and Costs of Parallel Programming
Amdahl's Law:
- Amdahl's Law
states that potential program
speedup is defined by the fraction of code (P) that can be parallelized:
1
speedup = --------
1 - P
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- If none of the code can be parallelized, P = 0 and the speedup = 1 (no
speedup). If all of the code is parallelized, P = 1 and the speedup is
infinite (in theory).
If 50% of the code can be parallelized, maximum speedup = 2, meaning
the code will run twice as fast.
- Introducing the number of processors performing the parallel fraction of
work, the relationship can be modeled by:
1
speedup = ------------
P + S
---
N
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where P = parallel fraction, N = number of processors and S = serial
fraction.
- It soon becomes obvious that there are limits to the scalability of
parallelism. For example, at P = .50, .90 and .99 (50%, 90% and 99% of
the code is parallelizable):
speedup
--------------------------------
N P = .50 P = .90 P = .99
----- ------- ------- -------
10 1.82 5.26 9.17
100 1.98 9.17 50.25
1000 1.99 9.91 90.99
10000 1.99 9.91 99.02
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- However, certain problems demonstrate increased performance by increasing
the problem size. For example:
2D Grid Calculations 85 seconds 85%
Serial fraction 15 seconds 15%
We can increase the problem size by doubling the grid dimensions and
halving the time step. This results in four times the number of grid
points and twice the number of time steps. The timings then look like:
2D Grid Calculations 680 seconds 97.84%
Serial fraction 15 seconds 2.16%
- Problems that increase the percentage of parallel time with their size
are more scalable than problems with a fixed percentage of
parallel time.
Complexity:
- In general, parallel applications are much more complex than corresponding
serial applications, perhaps an order of magnitude. Not only do you have
multiple instruction streams executing at the same time, but you also have
data flowing between them.
- The costs of complexity are measured in programmer time in virtually every
aspect of the software development cycle:
- Design
- Coding
- Debugging
- Tuning
- Maintenance
- Adhering to "good" software development practices is essential when
when working with parallel applications - especially if somebody besides
you will have to work with the software.
Portability:
- Thanks to standardization in several APIs, such as MPI, POSIX threads,
HPF and OpenMP, portability issues with parallel programs are not as
serious as in years past. However...
- All of the usual portability issues associated with serial programs
apply to parallel programs. For example, if you use vendor "enhancements"
to Fortran, C or C++, portability will be a problem.
- Even though standards exist for several APIs, implementations will differ
in a number of details, sometimes to the point of requiring code
modifications in order to effect portability.
- Operating systems can play a key role in code portability issues.
- Hardware architectures are characteristically highly variable and can
affect portability.
Resource Requirements:
- The primary intent of parallel programming is to decrease execution
wall clock time, however in order to accomplish this, more CPU time
is required. For example, a parallel code that runs in 1 hour on 8
processors actually uses 8 hours of CPU time.
- The amount of memory required can be greater for parallel codes than
serial codes, due to the need to replicate data and for overheads
associated with parallel support libraries and subsystems.
- For short running parallel programs, there can actually be a decrease
in performance compared to a similar serial implementation. The overhead
costs associated with setting up the parallel environment, task creation,
communications and task termination can comprise a significant portion of
the total execution time for short runs.
Scalability:
- The ability of a parallel program's performance to scale is a re