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.. _jit-help:
========
JIT help
========
.. note:: this is from ``pypy --jit help``
Advanced JIT options
====================
``<pypy> --jit`` [*options*] where *options* is a comma-separated list of
``OPTION=VALUE``:
decay=N
amount to regularly decay counters by (0=none, 1000=max) (default 40). This
value is used to reduce the JIT counters every 32 minor collections,
according to the formula ``val *= 1.0 - (decay / 1000)``. This avoids JIT
compilation of rare paths even on long-running programs.
disable_unrolling=N
after how many operations we should not unroll (default 200)
enable_opts=N
INTERNAL USE ONLY (MAY NOT WORK OR LEAD TO CRASHES): optimizations to
enable, or all =
intbounds:rewrite:virtualize:string:pure:earlyforce:heap:unroll (default
all)
function_threshold=N
number of times a function must run for it to become traced from start
(default 1619)
inlining=N
inline python functions or not (1/0) (default 1)
loop_longevity=N
a parameter controlling how long loops will be kept before being freed,
an estimate (default 1000)
max_retrace_guards=N
number of extra guards a retrace can cause (default 15)
max_unroll_loops=N
number of extra unrollings a loop can cause (default 0)
max_unroll_recursion=N
how many levels deep to unroll a recursive function (default 7)
retrace_limit=N
how many times we can try retracing before giving up (default 0)
threshold=N
number of times a loop has to run for it to become hot (default 1039)
trace_eagerness=N
number of times a guard has to fail before we start compiling a bridge
(default 200)
trace_limit=N
number of recorded operations before we abort tracing with ABORT_TOO_LONG
(default 6000)
vec=N
turn on the vectorization optimization (vecopt). Supports x86 (SSE 4.1),
powerpc (SVX), s390x SIMD (default 0)
vec_all=N
try to vectorize trace loops that occur outside of the numpypy library
(default 0)
vec_cost=N
threshold for which traces to bail. Unpacking increases the counter,
vector operation decrease the cost (default 0)
off
turn off the JIT
help
print this page
The :ref:`pypyjit<jit-hooks>` module can be used to control the JIT from inside
pypy
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