The previous array solution demonstrated static load balancing: Each task has a fixed amount of work to do. receive left endpoint from right neighbor Computer Architecture, Organization, Parallel A more optimal solution might be to distribute more work with each job. Debugging parallel codes can be incredibly difficult, particularly as codes scale upwards. Data transfer usually requires cooperative operations to be performed by each process. Another problem that's easy to parallelize: All point calculations are independent; no data dependencies, Work can be evenly divided; no load balance concerns, No need for communication or synchronization between tasks, Divide the loop into equal portions that can be executed by the pool of tasks, Each task independently performs its work, One task acts as the master to collect results and compute the value of PI. Now, highly performing computer system is obtained by using multiple processors, and most important and demanding applications are written as parallel programs. –Parallel Computer Hardware –+ Operating System (+Middleware) –(+ Programming Model) •Historically, architectures and programming models were coupled tightly –Architecture designed for PM (or vice versa) Torsten Hoefler: CS 498 Hot Topics in HPC 45 send each WORKER starting info and subarray if I am MASTER Therefore, network communications are required to move data from one machine to another. An audio signal data set is passed through four distinct computational filters. Memory is scalable with the number of processors. Holds pool of tasks for worker processes to do. Therefore, nowadays more and more transistors, gates and circuits can be fitted in the same area. In this programming model, processes/tasks share a common address space, which they read and write to asynchronously. 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. The distributed memory component is the networking of multiple shared memory/GPU machines, which know only about their own memory - not the memory on another machine. Calculate the potential energy for each of several thousand independent conformations of a molecule. Typically used to serialize (protect) access to global data or a section of code. This type of instruction level parallelism is called superscalar execution. A thread's work may best be described as a subroutine within the main program. 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. These implementations differed substantially from each other making it difficult for programmers to develop portable applications. Then, multiple CPUs were incorporated into a node. endif, Introduction to Parallel Computing Tutorial, LLNL Covid-19 HPC Resource Guide for New Livermore Computing Users, Livermore Computing PSAAP3 Quick Start Tutorial, Distributed Memory / Message Passing Model, http://en.wikipedia.org/wiki/John_von_Neumann, https://en.wikipedia.org/wiki/Coarray_Fortran, https://en.wikipedia.org/wiki/Global_Arrays, http://en.wikipedia.org/wiki/List_of_file_systems#Distributed_parallel_file_systems, https://hpc.llnl.gov/software/development-environment-software, https://computing.llnl.gov/tutorials/totalview/, http://www.cs.uoregon.edu/research/tau/docs.php, MPI Concurrent Wave Equation Program in C, MPI Concurrent Wave Equation Program in Fortran, http://www-users.cs.umn.edu/~karypis/parbook/, https://ipcc.cs.uoregon.edu/curriculum.html, https://sites.google.com/lbl.gov/cs267-spr2020, https://developer.nvidia.com/udacity-cs344-intro-parallel-programming. Often, a serial section of work must be done. else if I am WORKER Author(s): Hesham El‐Rewini; ... Computer architecture deals with the physical configuration, logical structure, formats, protocols, and operational sequences for processing data, controlling the configuration, and controlling the operations over a computer. 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). Operating systems can play a key role in code portability issues. Threaded implementations are not new in computing. There are two basic ways to partition computational work among parallel tasks: In this type of partitioning, the data associated with a problem is decomposed. "Introduction to Parallel Computing", Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar. Keeping data local to the process that works on it conserves memory accesses, cache refreshes and bus traffic that occurs when multiple processes use the same data. Most modern computers, particularly those with graphics processor units (GPUs) employ SIMD instructions and execution units. The text is written for designers, programmers, and engineers who need to understand these issues at a fundamental level in order to utilize the full power afforded by parallel computation. Wonder why? Various mechanisms such as locks / semaphores are used to control access to the shared memory, resolve contentions and to prevent race conditions and deadlocks. Other tasks can attempt to acquire the lock but must wait until the task that owns the lock releases it. On distributed memory machines, memory is physically distributed across a network of machines, but made global through specialized hardware and software. 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. For example: Web search engines, web based business services, Management of national and multi-national corporations, Advanced graphics and virtual reality, particularly in the entertainment industry, Networked video and multi-media technologies. Undoubtedly, the first step in developing parallel software is to first understand the problem that you wish to solve in parallel. These implementations differed substantially from each other making it difficult for programmers to develop portable threaded applications. A parallel solution will involve communications and synchronization. Cache coherent means if one processor updates a location in shared memory, all the other processors know about the update. Ultimately, it may become necessary to design an algorithm which detects and handles load imbalances as they occur dynamically within the code. This varies, depending upon who you talk to. Although machines built before 1985 are excluded from detailed analysis in this survey, it is interesting to note that several types of parallel computer were constructed in the United Kingdom Well before this date. On distributed memory architectures, the global data structure can be split up logically and/or physically across tasks. Involves only those tasks executing a communication operation. if I am MASTER Introducing the number of processors performing the parallel fraction of work, the relationship can be modeled by: where P = parallel fraction, N = number of processors and S = serial fraction. Each task owns an equal portion of the total array. I/O that must be conducted over the network (NFS, non-local) can cause severe bottlenecks and even crash file servers. Because the amount of work is equal, load balancing should not be a concern, Master process sends initial info to workers, and then waits to collect results from all workers, Worker processes calculate solution within specified number of time steps, communicating as necessary with neighbor processes. Thanks to standardization in several APIs, such as MPI, POSIX threads, and OpenMP, portability issues with parallel programs are not as serious as in years past. Dynamic load balancing occurs at run time: the faster tasks will get more work to do. Today, commercial applications provide an equal or greater driving force in the development of faster computers. Complex, large datasets, and their management can be organized only and only using parallel computing’s approach. else receive results from WORKER 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. find out if I am MASTER or WORKER Implement as a Single Program Multiple Data (SPMD) model - every task executes the same program. A parallel program is a program that uses the provided parallel hardware to execute a computation more quickly. It is not intended to cover Parallel Programming in depth, as this would require significantly more time. Cache coherency is accomplished at the hardware level. Vendor and "free" implementations are now commonly available. The best performance is achieved by an intermediate action plan that uses resources to utilize a degree of parallelism and a degree of locality. 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. Ensures the effective utilization of the resources. Often requires "serialization" of segments of the program. Are there areas that are disproportionately slow, or cause parallelizable work to halt or be deferred? The network "fabric" used for data transfer varies widely, though it can be as simple as Ethernet. Machine memory was physically distributed across networked machines, but appeared to the user as a single shared memory global address space. References are included for further self-study. The parallel I/O programming interface specification for MPI has been available since 1996 as part of MPI-2. Introduction to parallel processing; Memory and input-output subsystems; Principles of pipelining and vector processing; Pipeline computers and vectorization methods; Structures and algorithms for array processors; SIMD computers and performance enhancement; Multiprocessor architecture and programming; Multiprocessing control and algorithms; Example multiprocessor systems; Data Flow … The image data can easily be distributed to multiple tasks that then act independently of each other to do their portion of the work. This book is released under a CC-BY license, thanks to a gift from the Saylor Foundation. Threads communicate with each other through global memory (updating address locations). The "right" amount of work is problem dependent. For example: However, certain problems demonstrate increased performance by increasing the problem size. Introduction to Parallel Computing George Karypis Parallel Programming Platforms. N-body simulations - particles may migrate across task domains requiring more work for some tasks. This chapter introduces the basic foundations of computer architecture in general and for high performance computer systems in particular. The problem is computationally intensive. When the last task reaches the barrier, all tasks are synchronized. The data parallel model demonstrates the following characteristics: Most of the parallel work focuses on performing operations on a data set. In most cases, serial programs run on modern computers "waste" potential computing power. Profilers and performance analysis tools can help here. Load Balancing and Domain Decomposition; Locality and Communication Optimizations; Module 9: Introduction to Shared Memory Multiprocessors Parallel programming answers questions such as, how to divide a computational problem into subproblems that can be executed in parallel. The growth in instruction-level-parallelism dominated the mid-80s to mid-90s. In this example, the amplitude along a uniform, vibrating string is calculated after a specified amount of time has elapsed. The elements of a 2-dimensional array represent the temperature at points on the square. Example: Web search engines/databases processing millions of transactions every second. This can be explicitly structured in code by the programmer, or it may happen at a lower level unknown to the programmer. SPMD is actually a "high level" programming model that can be built upon any combination of the previously mentioned parallel programming models. Later on, 64-bit operations were introduced. also high speed computers are needed to process huge amount of data within a specified time. Parallel Computer Architecture. Usually comprised of multiple CPUs/processors/cores, memory, network interfaces, etc. Examples are available in the references. Two types of scaling based on time to solution: strong scaling and weak scaling. There are a number of important factors to consider when designing your program's inter-task communications: Worker Process: repeatedly does the following. Differs from earlier computers which were programmed through "hard wiring". In mid-80s, microprocessor-based computers consisted of. Sparse arrays - some tasks will have actual data to work on while others have mostly "zeros". For example: GPFS: General Parallel File System (IBM). That is, tasks do not necessarily have to execute the entire program - perhaps only a portion of it. The amount of time required to coordinate parallel tasks, as opposed to doing useful work. Relatively large amounts of computational work are done between communication/synchronization events, Implies more opportunity for performance increase. Section II: Parallel Architectures •What is a parallel platform? One of the more widely used classifications, in use since 1966, is called Flynn's Taxonomy. Few (if any) actual examples of this class of parallel computer have ever existed. Because each processor has its own local memory, it operates independently. multiple cryptography algorithms attempting to crack a single coded message. By the time the fourth segment of data is in the first filter, all four tasks are busy. All of these tools have a learning curve associated with them - some more than others. From a programming perspective, threads implementations commonly comprise: A library of subroutines that are called from within parallel source code, A set of compiler directives imbedded in either serial or parallel source code. The use of many transistors at once (parallelism) can be expected to perform much better than by increasing the clock rate. Often it is more efficient to package small messages into a larger message, thus increasing the effective communications bandwidth. Then, individual CPUs were subdivided into multiple "cores", each being a unique execution unit. Using "compiler directives" or possibly compiler flags, the programmer explicitly tells the compiler how to parallelize the code. Author: Blaise Barney, Livermore Computing (retired). For example, both Fortran (column-major) and C (row-major) block distributions are shown: Notice that only the outer loop variables are different from the serial solution. Like SPMD, MPMD is actually a "high level" programming model that can be built upon any combination of the previously mentioned parallel programming models. HDFS: Hadoop Distributed File System (Apache), PanFS: Panasas ActiveScale File System for Linux clusters (Panasas, Inc.). As with the previous example, parallelism is inhibited. Fast Download speed and ads Free! Adaptive grid methods - some tasks may need to refine their mesh while others don't. The first task to acquire the lock "sets" it. Parallel overhead can include factors such as: Refers to the hardware that comprises a given parallel system - having many processing elements. Distributed memory systems require a communication network to connect inter-processor memory. Since it is desirable to have unit stride through the subarrays, the choice of a distribution scheme depends on the programming language. Each thread has local data, but also, shares the entire resources of. May be possible to restructure the program or use a different algorithm to reduce or eliminate unnecessary slow areas, Identify inhibitors to parallelism. As time progresses, each process calculates its current state, then exchanges information with the neighbor populations. The value of PI can be calculated in various ways. Using the world's fastest and largest computers to solve large problems. 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. Periods of computation are typically separated from periods of communication by synchronization events. 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. Examples: Memory-cpu bus bandwidth on an SMP machine, Amount of memory available on any given machine or set of machines. left_neighbor = mytaskid - 1 The following are the different trends in which the parallel computer architecture is used. Department of Energy's National Nuclear Security Administration. Loops (do, for) are the most frequent target for automatic parallelization. The calculation of the minimum energy conformation is also a parallelizable problem. multiple frequency filters operating on a single signal stream. The following sections describe each of the models mentioned above, and also discuss some of their actual implementations. Both of the two scopings described below can be implemented synchronously or asynchronously. This task can then safely (serially) access the protected data or code. In almost all applications, there is a huge demand for visualization of computational output resulting in the demand for development of parallel computing to increase the computational speed. Hence, the concept of cache coherency does not apply. Modern computers, even laptops, are parallel in architecture with multiple processors/cores. The ability of a parallel program's performance to scale is a result of a number of interrelated factors. Growth in compiler technology has made instruction pipelines more productive. If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation. Message Passing Interface (MPI) on SGI Origin 2000. The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well … 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. receive starting info and subarray from MASTER In most cases the overhead associated with communications and synchronization is high relative to execution speed so it is advantageous to have coarse granularity. A single computer with multiple processors/cores, An arbitrary number of such computers connected by a network. Virtually all stand-alone computers today are parallel from a hardware perspective: Multiple functional units (L1 cache, L2 cache, branch, prefetch, decode, floating-point, graphics processing (GPU), integer, etc.). Most of these will be discussed in more detail later. Static load balancing is not usually a major concern if all tasks are performing the same amount of work on identical machines. Programmer responsibility for synchronization constructs that ensure "correct" access of global memory. Generally, the history of computer architecture has been divided into four generations having following basic technologies −. A set of tasks work collectively on the same data structure, however, each task works on a different partition of the same data structure. Other than pipelining individual instructions, it fetches multiple instructions at a time and sends them in parallel to different functional units whenever possible. Each processor can rapidly access its own memory without interference and without the overhead incurred with trying to maintain global cache coherency. In 1992, the MPI Forum was formed with the primary goal of establishing a standard interface for message passing implementations. Take advantage of optimized third party parallel software and highly optimized math libraries available from leading vendors (IBM's ESSL, Intel's MKL, AMD's AMCL, etc.). The nomenclature is confused at times. Implies high communication overhead and less opportunity for performance enhancement. do until no more jobs In an environment where all tasks see the same file space, write operations can result in file overwriting. A parallel program consists of multiple tasks running on multiple processors. if mytaskid = last then right_neighbor = first SINGLE PROGRAM: All tasks execute their copy of the same program simultaneously. This seminal work presents the only comprehensive integration of significant topics in computer architecture and parallel algorithms. Read operations can be affected by the file server's ability to handle multiple read requests at the same time. 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, If cache coherency is maintained, then may also be called CC-NUMA - Cache Coherent NUMA, 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. initialize the array Shared memory architecture - which task last stores the value of X. Parallel computers can be built from cheap, commodity components. There are two major factors used to categorize such systems: the processing units themselves, and the interconnection network that ties them together. Load balancing refers to the practice of distributing approximately equal amounts of work among tasks so that all tasks are kept busy all of the time. Therefore, more operations can be performed at a time, in parallel. However, resources are needed to support each of the concurrent activities. The primary intent of parallel programming is to decrease execution wall clock time, however in order to accomplish this, more CPU time is required. Worker process receives info, performs its share of computation and sends results to master. Distributed memory architectures - communicate required data at synchronization points. endif, #In this example the master participates in calculations, send left endpoint to left neighbor For array/matrix operations where each task performs similar work, evenly distribute the data set among the tasks. Each part is further broken down to a series of instructions. Cost effectiveness: can use commodity, off-the-shelf processors and networking. Refers to a parallel system's (hardware and/or software) ability to demonstrate a proportionate increase in parallel speedup with the addition of more resources. Arrays elements are evenly distributed so that each process owns a portion of the array (subarray). Network fabric—different platforms use different networks. The computation on each array element is independent from other array elements. During the past 20+ years, the trends indicated by ever faster networks, distributed systems, and multi-processor computer architectures (even at the desktop level) clearly show that, In this same time period, there has been a greater than. –The network is central to parallel computer architecture and its importance grows •“The networks of today’s HPC systems easily cost more than half of the system and for Exascale, the network might be by far the dominating cost.” Torsten Hoefler: CS 498 Hot Topics in HPC 26 Since then, virtually all computers have followed this basic design: Read/write, random access memory is used to store both program instructions and data, Program instructions are coded data which tell the computer to do something, Data is simply information to be used by the program, Control unit fetches instructions/data from memory, decodes the instructions and then, Arithmetic Unit performs basic arithmetic operations, Input/Output is the interface to the human operator. receive from MASTER next job, send results to MASTER Consider the Monte Carlo method of approximating PI: The ratio of the area of the circle to the area of the square is: Note that increasing the number of points generated improves the approximation. 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. adds a new dimension in the development of computer system by using more and more number of processors The computational problem should be able to: Be broken apart into discrete pieces of work that can be solved simultaneously; Execute multiple program instructions at any moment in time; Be solved in less time with multiple compute resources than with a single compute resource. endif, find out if I am MASTER or WORKER The 2-D heat equation describes the temperature change over time, given initial temperature distribution and boundary conditions. In general, parallel applications are much more complex than corresponding serial applications, perhaps an order of magnitude. Resources are also needed to allocate local storage. Goal is to run the same problem size faster, Perfect scaling means problem is solved in 1/P time (compared to serial), Goal is to run larger problem in same amount of time, Perfect scaling means problem Px runs in same time as single processor run.