.. _jit-help: ======== JIT help ======== .. note:: this is from ``pypy --jit help`` Advanced JIT options ==================== `` --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` module can be used to control the JIT from inside pypy