With the increasing programmability of GPUs, the application capabilities of GPUs have gone far beyond graphics rendering tasks. Research on using GPUs to perform general-purpose computing has gradually become active, and the use of GPUs for computing beyond graphics rendering has become GPGPU (General Purpose CompuTIng). On graphics processing units (GPU-based general-purpose calculations). At the same time, the CPU has encountered some obstacles. In order to pursue commonality, the CPU mainly uses most of the transistors for constructing control circuits (such as branch prediction, etc.) and the Cache, and only a small number of transistors complete the actual calculation. The CPU + GPU is a powerful combination because the CPU contains several cores optimized for serial processing, while the GPU is made up of thousands of smaller, more power-efficient cores designed to provide strong parallel performance. And design. The serial part of the program runs on the CPU, while the parallel part runs on the GPU. The GPU has matured and can easily execute various applications in real life, and the program has run far faster than when using multi-core systems. The future computing architecture will be a hybrid system with parallel core GPUs and multicore CPUs running together. Although GPUs are not suitable for solving all problems, we find that those scientific propositions that are costly for computing power have natural "" characteristics. Such programs have extremely high computational density, concurrent thread counts, and frequent memory accesses at run-time, and they are heavily involved in audio processing, visual simulation, and molecular dynamics modeling and financial risk assessment. If this problem can be successfully transferred to a GPU-based computing environment, it will bring us a more efficient solution. Traditionally GPUs are not good at running branch code, but ATI and NVIDIA have improved their internal architecture over a long period of time to allow GPUs to run complex code such as branches and loops more efficiently. At the same time, because the GPU belongs to the parallel machine category, the same operations can be applied to each data element, and they can achieve the best performance. In a CPU programming environment, writing a program that has a different number of inputs for each input data element is easy, but there is still a lot of trouble on a parallel GPU. The common data structure is one of the biggest difficulties in GPU programming. The data structures frequently used by CPU programmers such as lists and trees are not easily implemented on GPUs. The GPU currently does not allow arbitrary memory access, and the GPU computing unit is designed to operate on a four-dimensional vector representing position and color. However, these do not stop the accelerated development of GPU programming, because the GPU is not really designed for general-purpose computing, and it takes some effort to allow the GPU to service general-purpose computing programs at high speed. These efforts were implemented individually by programmers in the past few years, and as ATI and NVIDIA began to see the hardware requirements of the high-performance computing market, we have seen whether the Fermi architecture adds universal second-level cache and unified addressing or the RV870 architecture is continuously optimized. LDS and amplifying the number of concurrent threads, these are changes made by the GPU's own hardware system in order to adapt to the future computing environment. In the GPU parallel programming process, OpenCL is a good choice. OpenCL, short for Open CompuTIng Language (Open Computing Language), is the first unified, free standard for general-purpose parallel programming of heterogeneous systems. OpenCL supports heterogeneous systems consisting of multi-core CPUs, GPUs, Cell type architectures, and other parallel devices such as signal processors (DSPs). The advent of OpenCL has made it easier for software developers to write high-performance servers, desktop computing systems, and handheld devices. OpenCL is composed of a language for writing kernel programs and an API for defining and controlling the platform. It provides two parallel computer systems based on task and data, so that the computation of GPU is not limited to the graphics area but can be more parallel. Calculations. However, it is difficult to develop a program that can run on heterogeneous platforms (platforms on CPUs and GPUs) through traditional methods. GPUs of different vendors and different product models generally have different architectures. It is very difficult to develop a software that can efficiently use all the computing resources of different platforms. The emergence of OpenCL effectively solved the problem of heterogeneous platforms. The OpenCL specification was introduced by the Khronos Group. OpenCL programs can run on multi-core CPUs as well as on GPUs. This fully demonstrates OpenCL's cross-platform nature and portability, and allows programmers to make full use of them. The GPU's powerful parallel computing capabilities, compared to the CPU, there are many characteristics of the GPU. l The number of cores owned by a GPU is much higher than that of high-end CPUs. Although each computing core of the GPU does not have a high operating frequency of each computing core of the CPU, the overall performance of the GPU, the chip area ratio, and the performance-power consumption ratio are much higher than those of the CPU, so the task performance of the parallel computing that handles more threads is higher. a lot of. l The GPU can hide global delays through interleaving between a large number of parallel threads. In addition, the GPU has a large number of registers, local memory, and cache to improve the access performance of external storage. l In conventional CPU operations, switching between threads requires a lot of overhead, so the efficiency of algorithms that turn on a large number of threads is very low. However, in the GPU, switching between threads is very inexpensive. l The GPU's computing power is much better than that of the CPU. OpenCL is an open industry standard that can be programmed for heterogeneous platforms consisting of different devices such as CPUs and GPUs. OpenCL is a language and a framework for parallel programming. Programmers can use OpenCL to write a generic program that can be executed on a GPU. OpenCL's technology core packages the following four models: Platform model: The OpenCL platform model defines the role of the host and device, providing an abstract hardware model for the OpenCL C functions (kernels) that programmers execute on the device. The platform model determines that the processors on the host can coordinate execution and that there are one or more processors that can execute OpenCL C code (devices). Execution Model: Defines how the OpenCL environment is configured on the host and how the kernel is executed on the device. This includes setting up the OpenCL context on the host, providing a mechanism for interaction between the host and the device, and defining the mode of arms that the kernel executes on the device. l Memory Model: Defines the abstract memory hierarchy used by the kernel. l Programming Model: Defines how the concurrency model maps to physical hardware. The OpenCL framework is divided into platform-level APIs and runtime APIs. Platform-level APIs allow applications to query platforms and devices, and they can be managed through context. The runtime API uses context to manage the execution of the kernel on the device. After programming with OpenCL, we can debug with gDEBugger, an advanced OpenCL and OpenGL debugger, analyzer, and memory analyzer. It can accomplish what other tools can't do: Track the activities of applications on OpenCL and OpenGL and discover what's happening inside the system implementation. Programmers can use gDEBugger in the following situations l Optimize OpenCL and OpenGL application performance. l Quickly find bugs related to OpenCL and OpenGL. l Improve program performance and robustness In the past, although the GPU and CPU were integrated on the same chip (GPGPU technology), the location of the memory to be located during the operation of the chip still had to go through complicated steps. This is because the memory pool of the CPU and GPU is still It is an independent operation. In order to solve the problem of independent computing between the two memory pools, when the CPU program needs to perform partial operations on the GPU, the CPU must copy all the data from the CPU memory to the GPU memory, and when the GPU When the operation is completed, these data must be copied back to the CPU memory. These steps will continue to consume time and program processing performance. In 2012, AMD teamed up with ARM, Qualcomm, Samsung, MediaTek and other vendors to establish the HSA (Heterogeneous Systems Architecture) Foundation, hoping to expand the new architecture of CPU and GPU cooperative computing, and to assist the heterogeneous software development environment for the development of this architecture. A few days ago, AMD further publicized the new technology of this computing architecture: hUMA (heterogeneous Uniform Memory Access). With hUMA, CPUs and GPUs can share the same memory space, and the CPU can directly access the GPU's memory address, rather than having to spend time on the GPU's computing data to write to the CPU. Recently, at the HotChips conference, AMD continuously announced the Steamroller architecture for desktop FX processors and the Jaguar architecture for low-power platforms. However, this is not the ultimate goal of AMD. They claim that the competition for processor speed is over and the future is coming. Belongs to HSA. In the course of computer development, in order to solve a variety of specific problems, there are constantly incompatible computing modules that have been added to the system. However, they are rarely examined from the perspective of global optimization. The fact that the overall efficiency of the computer is low is a direct result of this design pattern. A common situation is that the software's computational load is scheduled to execute inefficiently on a computing device that is not suitable for the current task. The HSA presents a new architecture that can be adapted to the computational tasks of various features. The HSA version can seamlessly share data between CPUs and GPUs without the need for memory copies and cache flushes because tasks are scheduled to the right processor with very low overhead. The net result is that the performance of the HSA version is 2.3 times higher and the power consumption is reduced by 2.4 times*. In comparison, both multi-core CPUs, GPUs, and even non-HSA hybrid CPUs and GPUs cannot achieve this level of performance. It is also important that you do not need to switch to a different programming model. You can implement the program just by simply extending C++. We are a professional screen protector manufacturer, providing one-stop product production solutions, providing exclusive OEM and ODM services. A wide range of products, including HD Clear Screen Protectors, Self-repair Screen Protectors, Anti-microbial Screen Protectors, Matte Screen Protectors, Privacy Screen Protectors and other series. Universal Screen Protector, TPU Screen Protector, Hydrogel Protective Film, Mobile Phone Screen Protector, Hydrogel Screen Protector, TPU Protective Film Shenzhen Jianjiantong Technology Co., Ltd. , https://www.jjtbackskin.com
We are committed to the development, production and sales of hydrogel screen protectors. We strictly control the screen protector production process to ensure that each product meets industry standards. If you want to know more products, please click the product details to view.
Whether you are a group or an individual, we will do our best to provide you with the best service and the most accurate and comprehensive product information!