By Dinkar Sitaram, Geetha Manjunath
Moving to the Cloud presents an in-depth creation to cloud computing types, cloud structures, program improvement paradigms, thoughts and applied sciences. The authors relatively research cloud systems which are in use at the present time. in addition they describe programming APIs and evaluate the applied sciences that underlie them. the fundamental foundations wanted for constructing either client-side and cloud-side functions protecting compute/storage scaling, info parallelism, virtualization, MapReduce, RIA, SaaS and Mashups are lined. ways to handle key demanding situations of a cloud infrastructure, resembling scalability, availability, multi-tenancy, defense and administration are addressed. The publication additionally lays out the foremost open concerns and rising cloud criteria that may force the ongoing evolution of cloud computing.
* comprises complicated case reports of cloud strategies via cloud specialists from Yahoo! , Amazon, Microsoft, IBM, Adobe and HP Labs.
* offers insights and methods for growing compelling wealthy patron purposes that have interaction with cloud services.
* Demonstrates and distinguishes positive aspects of alternative cloud structures utilizing easy to advanced API programming examples.
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Extra resources for Moving To The Cloud: Developing Apps in the New World of Cloud Computing
Sample text
C -I . c -I. –I/opt/AMDAPP/include –L/opt/AMDAPP/lib/x86_64 –lOpenCL Regardless of the platform, a binary executable user_buffer would be deposited locally. Running the application on both platforms, we would get the following result: Number of OpenCL platforms found: 1 Kernel name: hello with arity: 1 About to create command queue and enqueue this kernel... Task has been enqueued successfully! Check passed! How it works… The application created a million of the UserData objects on the host. Refer to the following code snippet: /* Prepare an array of UserData via dynamic memory allocation */ UserData* ud_in = (UserData*) malloc( sizeof(UserData) * DATA_SIZE); // input to device UserData* ud_out = (UserData*) malloc( sizeof(UserData) * DATA_SIZE); // output from device for( int i = 0; i < DATA_SIZE; ++i) { (ud_in + i)->x = i; (ud_in + i)->y = i; (ud_in + i)->z = i; (ud_in + i)->w = 3 * i; } 47 Understanding OpenCL Data Transfer and Partitioning The application then sends it to the device for computation after the program and kernel objects have been initialized, and we assign the recently created UDObj memory object to the kernel as its argument.
I/opt/AMDAPP/include –L/opt/AMDAPP/lib/x86_64 –lOpenCL Regardless of the platform, a binary executable user_buffer would be deposited locally. Running the application on both platforms, we would get the following result: Number of OpenCL platforms found: 1 Kernel name: hello with arity: 1 About to create command queue and enqueue this kernel... Task has been enqueued successfully! Check passed! How it works… The application created a million of the UserData objects on the host. Refer to the following code snippet: /* Prepare an array of UserData via dynamic memory allocation */ UserData* ud_in = (UserData*) malloc( sizeof(UserData) * DATA_SIZE); // input to device UserData* ud_out = (UserData*) malloc( sizeof(UserData) * DATA_SIZE); // output from device for( int i = 0; i < DATA_SIZE; ++i) { (ud_in + i)->x = i; (ud_in + i)->y = i; (ud_in + i)->z = i; (ud_in + i)->w = 3 * i; } 47 Understanding OpenCL Data Transfer and Partitioning The application then sends it to the device for computation after the program and kernel objects have been initialized, and we assign the recently created UDObj memory object to the kernel as its argument.
This API returns a cl_kernel, which represents the kernel object when successful. This API provides the programmer with an option of not transforming every kernel function in the program into actual OpenCL kernel objects ready for execution. But if you wish to simply transform all kernel functions in the program into kernel objects, then clCreateKernelsInProgram is the API to use: cl_int clCreateKernelsInProgram(cl_program program, cl_uint num_kernels, cl_kernel* kernels, cl_uint* num_kernels_ret) You use this API to ask OpenCL to create and load the kernels into the kernels argument, and you hint to the OpenCL compiler how many kernels you're expecting with the num_kernels argument.