Creating a cake is analogous to doing three concurrent iterations of a loop. In the cake-making loop, identify and characterize instances of data and task-level parallelism
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Creating a cake is analogous to doing three concurrent iterations of a loop. In the cake-making loop, identify and characterize instances of data and task-level parallelism.
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- Baking a cake is like parallel computing three loop iterations. Explain why the cake-making loop has data-level and task-level parallelism.Think of three simultaneous loop iterations as creating a cake. Find and describe parallelism at the data and task levels in the cake-baking process.Explain the concept of dynamic multithreading and its benefits in improving program performance.
- Explain the difference between data parallelism and task parallelism.The task of making a cake may be compared to the computation of three loop iterations on a parallel computer. Explain how the cake-making loop benefits from both data-level and task-level parallelism.Find examples of task-level and data-level parallelism in the cake-making loop, and then explain why those examples are there.
- Explain the challenges and best practices for serializing complex data structures, such as graphs and trees, in a way that ensures consistency upon deserialization.How does the concept of 'multi-threading' contribute to improving the performance of software applications? Discuss the advantages and challenges associated with implementing multi-threading in concurrent programming."Consider baking a cake to be the parallel equivalent of running three iterations of a loop. In the cake-making loop, look for and describe instances of data-level and task-level parallelism.
- Think of baking a cake as being comparable to computing three iterations of a loop in parallel. In the cake-making loop, look for instances of task-level parallelism and data-level parallelism and justify their existence.Which of the following statements about parallelism is/are FALSE? 1. In data parallelism, the same task is executed on the same data at the same time 2. A task dependency graph allows one to plan parallel tasks 3. If a task depends on the results of another task, they cannot run in parallel Select one: a. 2 only b. 1 and 3 c. 1 and 2 d. 1 only e. 2 and 3 Clear my choice Which of the following statements on the reduction parallel pattern is/are TRUE? 1. A reduction pattern combines data elements using an associative operator into a single element 2. Synchronisation is not required when accessing data in a reduction pattern 3. A reduction pattern typically processes half the number of elements at each step Select one: a. 2 only b. 2 and 3 c. 1 and 3 d. 1 and 2 e. 1 only Clear my choice Which of the following statements related to data on the host and device is/are FALSE? 1. Kernel arguments must be set explicitly using cl::Kernel::setArg() on the host 2. The device initiates data…Explain the concept of multithreading and its advantages in terms of concurrency and resource utilization.