Loops induced with prange can be used for embarrassingly parallel computation and also reductions. To indicate that a loop should be executed in parallel the numba.
Numba parallel execution also has support for explicit parallel loop declaration similar to that in OpenMP.
#Intel c compiler svml math functions code#
If code contains operations that are parallelisable and supported Numba can compile a version that will run in parallel on multiple native threads no GIL! This parallelisation is performed automatically and is enabled by simply adding the parallel keyword argument. This can be done by supplying a set of LLVM fast-math flags to fastmath. In some cases you may wish to opt-in to only a subset of possible fast-math optimizations. The way to achieve this behaviour in Numba is through the use of the fastmath keyword argument. As a result it is possible to relax some numerical rigour with view of gaining additional performance. In certain classes of applications strict IEEE compliance is less important. The above run at almost identical speeds when decorated yar iska part 2 njitwithout the decorator the vectorized function is a couple of orders of magnitude faster. Whilst NumPy has developed a strong idiom around the use of vector operations, Numba is perfectly happy with loops too.įor example. Whilst the use of looplifting in object mode can enable some performance increase, getting functions to compile under no python mode is really the key to good performance. The information presented here is to demonstrate features, not to act as canonical guidance!Ī common pattern is to decorate functions with jit as this is the most flexible decorator offered by Numba. A reasonably effective approach to achieving high performance code is to profile the code running with real data and use that to guide performance tuning.
Two examples are used, both are entirely contrived and exist purely for pedagogical reasons to motivate discussion.Īll performance numbers are indicative only and unless otherwise stated were taken from running on an Intel i CPU 4 hardware threads with an input of np. This is a short guide to features present in Numba that can help with obtaining the best performance from code.