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Georg Hager
2019-03-14 12:07:14 +01:00
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@@ -9,10 +9,10 @@ It contains the following streaming kernels with corresponding data access patte
* sum (L1): Vector reduction: `s += a`. Load only.
* copy (L1, S1, WA): Classic memcopy: `a = b`.
* update (L1, S1): Update vector: `a = a * scalar`. Also load + store but without write allocate.
* triad (L2, S1, WA): Stream triad - `a = b + c * scalar`.
* daxpy (L2, S1): Daxpy - `a = a + b * scalar`.
* striad (L3, S1, WA): Schoenauer triad - `a = b + c * d`.
* sdaxpy (L3, S1): Schoenauer triad without write allocate - `a = a + b * c`.
* triad (L2, S1, WA): Stream triad: `a = b + c * scalar`.
* daxpy (L2, S1): Daxpy: `a = a + b * scalar`.
* striad (L3, S1, WA): Schoenauer triad: `a = b + c * d`.
* sdaxpy (L3, S1): Schoenauer triad without write allocate: `a = a + b * c`.
As added benefit the code is a blueprint for a minimal benchmarking application with a generic makefile and modules for aligned array allocation, accurate timing and affinity settings. Those components can be used standalone in your own project.