What problems fit to GPU? — GPU programming

## What are GPUs not good for?  Not all programming problems can efficiently leverage the parallelism offered by GPUs. Some types of problems that do not fit well on a GPU include: - **Sequential tasks**: Problems that require a series of dependent steps, where each step relies on the outcome of the previous step, are not well-suited for parallel processing. Examples include recursive algorithms, certain dynamic programming problems, and some graph traversal algorithms. - **Fine-grained branching**: GPUs perform best when the code being executed across different threads follows a similar control flow. When there is extensive branching (i.e., many `if` statements) within a kernel or algorithm, performance may suffer due to the divergence in execution paths among the GPU threads. - **Low arithmetic intensity**: GPUs excel at performing a large number of mathematical operations quickly. If a problem has low arithmetic intensity (i.e., a low ratio of arithmetic operations to memory accesses), the GPU may not be able to efficiently utilize its computational power, leading to underperformance. - **Small data sets**: If the problem involves a small data set that does not require significant parallelism, using a GPU may not result in noticeable performance gains. In such cases, the overhead of transferring data between the CPU and GPU, and the time spent initializing the GPU, may outweigh any potential benefits. - **Limited parallelism**: Some algorithms have inherent limitations on the degree of parallelism that can be achieved. In these cases, using a GPU may not lead to significant performance improvements. - **Memory-bound problems**: GPUs generally have less memory available compared to CPUs, and their memory bandwidth can be a limiting factor. If a problem requires a large amount of memory or involves memory-intensive operations, it may not be well-suited for a GPU. … Solution The right answer is option 3. GPUs do not handle recursion and branching as effectively as more data-heavy algorithms. Keypoints - GPUs excel in processing tasks with high data parallelism, such as large-scale matrix operations, Fourier transforms, and big data analytics. - GPUs struggle with sequential tasks, problems with extensive control flow divergence, low arithmetic intensity tasks, small data sets, and memory-bound problems.