Software utlizing multicore

Alternatively, if this function is built into the hardware, how do the cores know which apps to execute, and when? I assume that more cores are better, but how does this work, exactly? Your CPU passes certain information about its own operating characteristics over to the motherboard UEFI, which then uses this information to initialize the motherboard and boot the system.

One of the critical components of the operating system is called the scheduler. If you wanted to make an analogy, you could compare a thread to a one step on an assembly line.

One step above the thread, we have the process. Processes are computer programs that are executed in one or more threads. In this simplified factory analogy, the process is the entire procedure for manufacturing the product, while the thread is each individual task.

Problem : CPUs can only execute one thread at a time. Each process requires at last one thread. How do we improve computer performance? For decades, Dennard Scaling was the gift that kept on giving.

If the computer is running quickly enough, its inability to handle more than one thread at a time becomes much less of a problem. The phases of software development discussed in MPP and a summary of each follows: Program analysis and high level design is a study of an application to determine where to add concurrency and a strategy for modifying the application to support concurrency.

Implementation and low level design is the selection of design patterns, algorithms, and data structures and subsequent software coding of the concurrency.

Debug comprises implementation of the concurrency in a manner that minimizes latent concurrency issues, enabling of an application to be easily scrutinized for concurrency issues, and techniques for finding concurrency issues. Performance concerns improving turnaround time or throughput of the application by finding and addressing the effects of bottlenecks involving communication, synchronization, locks, load balancing, and data locality.

Existing technology includes the programming models and multicore architectures detailed in the guide and are limited to a few that are in wide use today. Existing software, also known as legacy software, is the currently used application as represented by its software code. Customers using existing software have chosen to evolve the implementation for new product development instead of re-implementing the entire application to enable multicore processors. Specifically, the benefits of this guide to the specific target audience are summarized below: Software developers who are experienced in sequential programming will benefit from reading this guide as it will explain new concepts which are essential when developing software targeted at multicore processors.

Engineering managers running technical teams of hardware and software engineers will benefit from reading this guide by becoming knowledgeable about development for multicore processors and the learning curve faced by their software developers. Project managers scheduling and delivering complex projects will benefit from reading this guide as it will enable them to appreciate, and appropriately schedule, projects targeting multicore processors.

Test engineers developing tests to validate functionality and verify performance will benefit from reading this guide as it will enable them to write more appropriate and more efficient tests for multicore-related projects. Figure 1: Code sample and steps to reproduce. Tags: Analog , Digital , Language.

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These cookies track visitors across websites and collect information to provide customized ads. Others Others. Typical applications used in the simulation environment consist of:. The decomposition of the legacy software application code would help identify software module that have a high hotspot probability factor which then would lead to increased focus on further computational resources dedicated to applied optimization techniques. Analysis will establish target gains to be achieved using parallelism.

Where parallelism has reached its peak performance gains this effort will then provide guidance to techniques beyond parallelism for increased gains.

The process of generating parallel code form the serial code comes with some risks that must be managed by the solution. Parallel techniques typically split serial code to a pool of worker threads, each operating independently, which then run to completion until the end of a parallel section. When processing is threaded across multiple cores, attention must be paid by the analyzer to data conflicts, race conditions, priority inversions or deadlocks.

The piece of code containing the concurrency issue is called a critical region. Typically when multiple threads are utilized to access common data, for data consistency, a mutex or lock is utilized to protect access to thread shared data. The use of locks brings inherent risk in a multithread environment which is familiar to developers of real time systems and embedded applications.

We have utilized non-blocking techniques in our own optimized parallel solutions to avoid these common multithreaded risks by using alternatives to locking mechanisms. The following paragraphs describe additional code optimization techniques for performance that we propose for a broad optimization solution in addition to serial to parallel conversion techniques.

These techniques do not perform explicit serial to parallel code conversion, and therefore avoid concurrency issues. Applications developed for bit operation are run on bit Windows operating systems using the WoW64 Windows bit on Windows bit subsystem. Since no structural changes to occur to the legacy code, there is a low risk of introducing concurrency based issues.

An advantage of using the bit version of Photoshop CS5 is to access amounts of RAM beyond what Photoshop can access as a bit application for platforms which are configured for more than 4 GB of memory.

Migrating code from bits to bits, similar to serial to parallel techniques, are development language specific. The following paragraphs describe migration procedures for various development languages. This compile option will test variables of the type int , long , and pointer for compliance with bit operation. COTS software tools such as PVS-Studio [3] are available to perform static analysis on legacy code to detect bit to bit migration issues.

Lint can be leveraged to identify suspicious and non-portable constructs as part of static code analysis to further ensure the legacy code is platform architecture neutral. In general, conversion of bit managed code C or VB. NET applications to bits should not be problematic. Managed code normally is converted to an intermediate language IL and that is processor independent and works on 32 and 64 bit platforms.

When decomposing a legacy project into language and functional modules, part of that effort is to identify 3rd party libraries provided without source code that are utilized as part of the project. When converting 3rd party libraries or components to bits, it may be determined if there are bit versions available. If not, alternative means are necessary similar to the descriptions in later paragraphs. The compiler is supported on IA and Intel 64 architectures, and some of the optimizations made by the compiler are targeted towards Intel hardware platforms.

This puts a constraint on the platform where the legacy applications are runtime evaluated for performance to execute as Intel based laptops, desktops, or servers. This is referred to in this document as implicit parallelization and occurs without changes to the legacy application source code. Automatic parallelization determines the loops that are good work sharing candidates, performs the data-flow analysis to verify correct parallel execution, and partitions the data for threaded code generation as is needed in programming with OpenMP [15] directives.

The OpenMP [15] and auto-parallelization applications provide the performance gains from shared memory on multiprocessor systems. Many general-purpose microprocessors today feature multimedia extensions that support SIMD single-instruction-multiple-data parallelism on relatively short vectors. By processing multiple data elements in parallel, these extensions provide a convenient way to utilize data parallelism in scientific, engineering, or graphical applications that apply a single operation to all elements in a data set, such as a vector or matrix.

Vectorization is a form of data-parallel programming. In this, the processor performs the same operation simultaneously on N data elements of a vector a one-dimensional array of scalar data objects such as floating point objects, integers, or double precision floating point objects. Details on vectorization using the Intel compiler are described by the citations [8] and [9]. A trade study of automatic serial-to-parallel techniques and solutions will be performed under this effort.

This section covers current areas of interest for the study. One part of the study will be the ways to divide an application over multiple processing cores by finding a means to automatically parallelize the sequential code. This is an active area of research and one of core areas in the solution space of this technology.

Parallel solutions are language dependent. No one technology is currently available for various development languages in managed or unmanaged code.

In contrast, C , VB. The following paragraphs describe the open source parallel technologies available in different development languages.

The specific parallel technology used is development language specific and will be selected based on an evaluation of suitability to task based on the legacy application to be converted and the development language where the hotspots exist. There is a wealth of resources available for converting serial application code to parallel implementations as described in the following paragraphs.

The OpenMP [15] API defines a portable, scalable model with a simple and flexible interface for developing parallel applications on platforms from the desktop to the supercomputer. On the Windows platform, OpenMP directives are preceded with a pragma construct which allows the original source code to remain unchanged. This is an important consideration when trying to achieve the goal of minimizing changes to legacy source code change to preserve as much as possible validation to the existing algorithms.

Rather than a library, the OpenMP standard is implemented and depends upon the compiler. This includes the omission of a task yield API to prevent busy waits on the worker threads and atomic operations [16].

The pros and cons of OpenMP are summarized at the referenced citation [17]. OpenMP-compliant implementations are not required to check for data dependencies, data conflicts, race conditions or deadlocks; any of which may occur in conforming programs.

In addition, compliant implementations are not required to check for code sequences that cause a program to be classified as non-OpenMP API conforming. What makes OpenMP particularly attractive is the simplicity of the range of statements, and the unified code for both serial and parallel applications allows OpenMP constructs to be treated as comments when sequential compilers are used.

OpenMP is a technology which has minimal impact to the existing legacy source code perhaps preserving the validation of the legacy code. PVS-Studio [3] provides static analysis tools for verifying OpenMP compliant, detecting some errors causing race conditions, ineffective use of processor time and so on.

The static analyzer works with the source code and reviews all the possible ways of program execution, so it can find some of these very rare errors. OpenCL includes a language based on C99 for writing kernels functions that execute on OpenCL devices , plus application programming interfaces APIs that are used to define and then control the platforms.

OpenCL provides parallel computing using task-based and data-based parallelism. OpenCL is an open standard maintained by the non-profit technology consortium Khronos Group. Therefore, OpenCL is not entirely platform independent. Depending upon the hardware platform selected, a GPU may exist as a resource to exploit. In general, GPU programming complexity poses a significant challenge for developers. It consists of a template-based runtime library to help you harness the latent performance of multi-core processors.

It is not available for managed code or Java. TBB is a library that assists in the application of multi-core performance enhancement techniques. Often improvements in performance for multi-core processors are achieved by implementing a few key points. There are a variety of approaches to parallel programming, ranging from using platform-dependent threading primitives to new languages.

The advantage of TBB is that it works at a higher level than raw threads, yet does not require exotic languages or compilers. Chiefly these include:. Intel TBB has been designed to be more conducive to application parallelization on client platforms such as laptops and desktops, going beyond data parallelism to be suitable for programs with nested parallelism, irregular parallelism and task parallelism.

The concepts behind Cilk Plus are simplification of adding parallelism. Intel Cilk Plus offers a quick and easy way to harness the power of both multi-core and vector processing. With the use of array notations application performance can be increased through vectorization as described in paragraph 2.

TBB will require more time consuming changes to the original code base with a potentially higher payoff in performance and features, while OpenMP gives a quicker payoff. Choosing threading approach is an important part of the parallel application design process. There is no single solution that fits all needs and development languages. Some require compiler support. Some are not portable or are not supported by the specialized threading analysis tools. TBB covers commonly used parallel design patterns and helps create scalable programs faster by providing concurrent data containers, synchronization primitives, parallel algorithms, and a scalable memory allocator.