TimeIt | simple C # library for mesure your application methods

From the cpython source code for sum sum initially seems to attempt a fast path that assumes all inputs are the same type. If that fails it will just iterate:

TL;DR: np.fromfile and np.frombuffer are not optimized to read many small buffers. You can load the whole file in a big buffer and then decode it very efficiently using Numba.

The main issue is that the benchmark measure overheads. Indeed, it perform a lot of system/C calls that are very inefficient. For example, on the 24 MiB file, the while loops calls 601_214 times np.fromfile and np.frombuffer. The timing on my machine are 10.5s for read_binary_npfromfile and 1.2s for read_binary_npfrombuffer. This means respectively 17.4 us and 2.0 us per call for the two function. Such timing per call are relatively reasonable considering Numpy is not designed to efficiently operate on very small arrays (it needs to perform many checks, call some functions, wrap/unwrap CPython types, allocate some objects, etc.). The overhead of these functions can change from one version to another and unless it becomes huge, this is not a bug. The addition of new features to Numpy and CPython often impact overheads and this appear to be the case here (eg. buffering interface). The point is that it is not really a problem because there is a way to use a different approach that is much much faster (as it does not pay huge overheads).

The main solution to write a fast implementation is to read the whole file once in a big byte buffer and then decode it using np.view. That being said, this is a bit tricky because of data alignment and the fact that nearly all Numpy function needs to be prohibited in the while loop due to their overhead. Here is an example:

It is going to be quite hard to get numpy to go as fast as the filtered python iterator because numpy processes whole structures that will inevitably be larger than the result of filtering sets.

Here is the best I could come up with to process the product of arrays in such a way that the result is filtered on unique combinations of distinct values:

This is a performance issue of the current Numpy implementation. I can also reproduce this problem on Windows (using an Intel Skylake Xeon processor with Numpy 1.20.3). np.logical_and(a, b) executes a very-inefficient scalar assembly code based on slow conditional jumps while np.logical_and(a.view(bool), b.view(bool)) executes relatively-fast SIMD instructions.

Currently, Numpy uses a specific implementation for bool-types. Regarding the compiler used, the general-purpose implementation can be significantly slower if the compiler used to build Numpy failed to automatically vectorize the code which is apparently the case on Windows (and explain why this is not the case on other platforms since the compiler is likely not exactly the same). The Numpy code can be improved for non-bool types. Note that the vectorization of Numpy is an ongoing work and we plan optimize this soon.

Here is the assembly code executed by np.logical_and(a, b):

The functions provided in the Python re module do not optimize based on anchors. In particular, functions that try to apply a regex at every position - .search, .sub, .findall etc. - will do so even when the regex can only possibly match at the beginning. I.e., even without multi-line mode specified, such that ^ can only match at the beginning of the string, the call is not re-routed internally. Thus:

Because your bins are the same for your 3 columns, use codes from cat accessor:

Here, copying of data doesn't play a big role: the bottle neck is fast how the tanh-function is evaluated. There are many algorithms: some of them are faster some of them are slower, some are more precise some less.

Different numpy-distributions use different implementations of tanh-function, e.g. it could be one from mkl/vml or the one from the gnu-math-library.

Depending on numba version, also either the mkl/svml impelementation is used or gnu-math-library.

The easiest way to look inside is to use a profiler, for example perf.

For the numpy-version on my machine I get:

The problem comes from the Numba implementation of np.convolve. This is a known issue. It turns out that the current Numba implementation is much slower than the one of Numpy (version <=0.54.1 tested on Windows).

On one hand, the Numpy implementation call correlate which itself performs a dot product that should be implemented by the fast BLAS library available on your system. On the other hand, the Numba implementation calls _get_inner_prod which use np.dot that should also use the same BLAS library (assuming a BLAS is detected which should be the case)...

That being said, there are multiple issues related to the dot product:

First of all, if the internal variable _HAVE_BLAS of numba/np/arraymath.py is manually disabled, Numba use a fallback implementation of the dot product supposed to be significantly slower. However, it turns out that using the fallback dot product implementation used by np.convolve result in a 5 times faster execution than with the BLAS wrapper on my machine! Using additionally the parameter fastmath=True in the njit Numba decorator results in an overall 8.7 times faster execution! Here is the testing code:

TL;DR: I made an experimental analysis on Numpy 1.21.1. Experimental results show that np.sum does NOT (really) make use of SIMD instructions: no SIMD instruction are used for integers, and scalar SIMD instructions are used for floating-point numbers! Moreover, Numpy converts the integers to 64-bits values for smaller integer types by default so to avoid overflows!

Note that this may not reflect all Numpy versions since there is an ongoing work to provide SIMD support for commonly used functions (the version Numpy 1.22.0rc1 not yet released continue this long-standing work). Moreover, the compiler or the processor used may significantly impact the results. The following experiments have been done using a Numpy retrieved from pip on a Debian Linux with a i5-9600KF processor.

For floating-point numbers, Numpy uses a pairwise algorithm which is known to be quite numerically stable while being relatively fast. This can be seen in the code, but also simply using a profiler: TYPE_pairwise_sum is the C function called to compute the sum at runtime (where TYPE is DOUBLE or FLOAT).

For integers, Numpy use a classical naive reduction. The C function called is ULONG_add_avx2 on AVX2-compatible machines. It also surprisingly convert items to 64-bit ones if the type is not np.int64.

Here is the hot part of the assembly code executed by the DOUBLE_pairwise_sum function