By Feuer A.R.
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MapS g’ :: s a → s c mapS (f’ . 14: Composing mapS with other stream functions. Since mapS has significant startup overheads compared to map, rewriting to reduce its applications may offer considerable increases in performance. In this sec56 tion we quantify these increases by comparing the execution times of programs which have been compiled several times, with rewriting enabled and with rewriting disabled. 00Ghz Intel Core 2 Duo CPU (6Mb L2 cache) and an Nvidia GeForce 8600 GTS/PCIe, and the average time was taken over five executions.
4 generateKernel is, as you might expect, implemented in terms of 45 generateIterativeKernel. • Kernel calls are instrumented with appropriate sizings – iterative kernels are handled as described earlier. The blocks of boilerplate code are then assembled around the invocations. 3. The CUDA compiler5 is called to compile the kernel and wrapper into a shared library. In the case of the wrapper, which is pure C, the host compiler will be invoked automatically by the CUDA compiler. The names of the wrapper and shared library are then returned as a tuple, so that the caller has enough information to utilise the generated code.
Return result assign :: ... → H () assign ... = do body ← get let assignment = ... 5: Using the State monad to build kernels. Here, evaluate is an arbitrary function that might handle addition, for example. State module. 39 generated variable, and returns that variable as its result. 4, but that it implements the accumulation we discussed in this and the previous section. 3 Name Generation When kernels are produced, the code generator creates a fresh name for each identifier it encounters. 6. 6: Using generated identifiers to construct a kernel.
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