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Shared memory is a powerful feature for writing well-optimized CUDA code. Throughput Reported by Visual Profiler, 9.1. Many software libraries and applications built on top of CUDA (e.g. Devices of compute capability 1.3 and higher provide native support for double-precision floating-point values (that is, values 64 bits wide). The compiler can optimize groups of 4 load and store instructions. A kernel to illustrate non-unit stride data copy. The only performance issue with shared memory is bank conflicts, which we will discuss later. In some instances, operations performed by automatic NUMA balancing may degrade the performance of applications running on NVIDIA GPUs. The number of copy engines on a GPU is given by the asyncEngineCount field of the cudaDeviceProp structure, which is also listed in the output of the deviceQuery CUDA Sample. Code that transfers data for brief use by a small number of threads will see little or no performance benefit. Single-precision floats provide the best performance, and their use is highly encouraged. Each floating-point arithmetic operation involves a certain amount of rounding. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. Shared memory enables cooperation between threads in a block. Table 2. Performance Improvements Optimizing C = AB Matrix Multiply However, as with APOD as a whole, program optimization is an iterative process (identify an opportunity for optimization, apply and test the optimization, verify the speedup achieved, and repeat), meaning that it is not necessary for a programmer to spend large amounts of time memorizing the bulk of all possible optimization strategies prior to seeing good speedups. Memory Access For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. In Unoptimized handling of strided accesses to global memory, the row-th, col-th element of C is obtained by taking the dot product of the row-th and col-th rows of A.