CUDA extends C by allowing the programmer to define C functions, called kernels, that, when called, are executed mn times in parallel by n different CUDA threads, as opposed to only once like regular C functions. Each of the threads that execute a kernel is given a unique thread ID that is accessible within the kernel through the built-in threadIdx variable. As an illustration, the above sample code adds two vectors A and B of size N and stores the result into vector C.
CUDA threads may access data from multiple memory spaces during their execution. Each thread has a private local memory. Each thread block has a shared memory visible to all threads of the block and with the same lifetime as the block. Finally, all threads have access to the same global memory.
There are also two additional read-only memory spaces accessible by all threads: the constant and texture memory spaces.
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The global, constant, and texture memory spaces are optimized for different memory usages. Texture memory also offers different addressing modes, as well as data filtering, for some specific data formats. The global, constant, and texture memory spaces are persistent across kernel launches by the same application. CUDA also assumes that both the host and the device maintain their own DRAM, referred to as host memory and device memory, respectively.
Therefore, a program manages the global, constant, and texture memory spaces visible to kernels through calls to the CUDA runtime described in Chapter 4. This includes device memory allocation and deallocation, as well as data transfer between host and device memory. Figure 4. The goal of the CUDA programming interface is to provide a relatively simple path for users familiar with the C programming language to easily write programs for execution by the device.
It consist of :. If none of them is present, the variable:. Compiling a CUDA program is not as straightforward as running a C compiler to convert source code into executable object code. The first step is to separate the source code for each target architecture. The generated host code is output either as C code that is left to be compiled using another tool or as object code directly by invoking the host compiler during the last compilation stage.
CUDA code should include the cuda. On the compilation command line, the cuda library should be specified to the linker on UNIX and Linux environments.
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Two steps are explained below. Use one host thread per device, since any given host thread can call cudaSetDevice at most one time. Applications that require tight coupling of the various CUDA devices within a sytem, these approaches may not be sufficent due to sychronization or communication with each other.
The CUDA Runtime now provides features in which single hos thread could easily launch work onto any devices it needed.
To acommplish this, a host thread can call cudaSetDevice at any time to change the currently active device. Also, host-thread can now control more than one device. Also, identification of compute intensive portion of the existing multi-threaded CPU code and port the code to GPU is easy without changing the inter-CPU-thread communication code unchanged. Only one context can be active on a GPU at any particular instant. Similarly, a CPU thread can have one active context at a time. A context is established during the program's first call to a function that changes state such as cudaMalloc ,etc.
The context is destroyed either with a cudaDeviceReset call or when the controlling CPU process exits.
For example, one can add GPU processing to an existing MPI application by porting the compute-intensive portions of the code without changing the communication structure. Even though a GPU can execute calls from one context at a time, it can belong to multiple contexts. For example, it is possible for several CPU threads to establish separate contexts with the same GPU though multiple CPU threads within the same process accessing the same GPU would normally share the same context by default. The GPU driver manages GPU switching between the contexts, as well as partitioning memory among the contexts GPU memory allocated in one context cannot be accessed from another context.
CUDA Toolkit 4. Unified Virtual Addressing UVA allows the system memory and the one or more device memories in a system to share a single virtual address space. This allows the CUDA Driver to determine the physical memory space to which a particular pointer refers by inspection, which simplifies the APIs of functions such as cudaMemcpy , since the application need no longer keep track of which pointers refer to which memory. In version 4. Licensed according to this deed. Virtually all semiconductor market domains, including PCs, game consoles, mobile handsets, servers, supercomputers, and networks, are converging to concurrent platforms.
There are two important reasons for this trend. First, these concurrent processors can potentially offer more effective use of chip space and power than traditional monolithic microprocessors for many demanding applications. Second, an increasing number of applications that traditionally used Application Specific Integrated Circuits ASICs are now implemented with concurrent processors in order to improve functionality and reduce engineering cost.
The real challenge is to develop applications software that effectively uses these concurrent processors to achieve efficiency and performance goals. The aim of this course is to provide students with knowledge and hands-on experience in developing applications software for processors with massively parallel computing resources.
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In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, and resource limitations of these processors. The target audiences of the course are students who want to develop exciting applications for these processors, as well as those who want to develop programming tools and future implementations for these processors.
The book targets specific hardware and evaluates performance based on this specific hardware. Therefore, the chances are that a lot of beginning parallel programmers can have access to Telsa GPU. Also, this book gives clear descriptions of Tesla GPU architecture, which lays a solid foundation for both beginning parallel programmers and experienced parallel programmers. The book can also serve as a good reference book for advanced parallel computing courses. Quick jump to page content.
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