This fixes compiling for older architectures (e.g. armv5tej).
According to [1], the limit of R0-R7 for the STR and LDR instructions is
tied to the Thumb instruction set and not any specific processor
architectures.
[1]: http://www.keil.com/support/man/docs/armasm/armasm_dom1361289906890.htm
When compiled with hard float the system should enable FP access when it
starts or else FP instructions lead to a fault. But this minimal port does
not enable (or use) FP and so, to keep it minimal, switch to use soft
floating point. (This became an issue due to the recent commit
34c04d2319 which saves/restores FP registers
in the NLR state.)
misc_aes.py and misc_mandel.py are adapted from sources in this repository.
misc_pystone.py is the standard Python pystone test. misc_raytrace.py is
written from scratch.
This benchmarking test suite is intended to be run on any MicroPython
target. As such all tests are parameterised with N and M: N is the
approximate CPU frequency (in MHz) of the target and M is the approximate
amount of heap memory (in kbytes) available on the target. When running
the benchmark suite these parameters must be specified and then each test
is tuned to run on that target in a reasonable time (<1 second).
The test scripts are not standalone: they require adding some extra code at
the end to run the test with the appropriate parameters. This is done
automatically by the run-perfbench.py script, in such a way that imports
are minimised (so the tests can be run on targets without filesystem
support).
To interface with the benchmarking framework, each test provides a
bm_params dict and a bm_setup function, with the later taking a set of
parameters (chosen based on N, M) and returning a pair of functions, one to
run the test and one to get the results.
When running the test the number of microseconds taken by the test are
recorded. Then this is converted into a benchmark score by inverting it
(so higher number is faster) and normalising it with an appropriate factor
(based roughly on the amount of work done by the test, eg number of
iterations).
Test outputs are also compared against a "truth" value, computed by running
the test with CPython. This provides a basic way of making sure the test
actually ran correctly.
Each test is run multiple times and the results averaged and standard
deviation computed. This is output as a summary of the test.
To make comparisons of performance across different runs the
run-perfbench.py script also includes a diff mode that reads in the output
of two previous runs and computes the difference in performance. Reports
are given as a percentage change in performance with a combined standard
deviation to give an indication if the noise in the benchmarking is less
than the thing that is being measured.
Example invocations for PC, pyboard and esp8266 targets respectively:
$ ./run-perfbench.py 1000 1000
$ ./run-perfbench.py --pyboard 100 100
$ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
Damien George
David Lechner
Paul m. p. P
Chris Mason