pbinfo-python-jit

Pbinfo Python JIT — Shellcode Escalation

When a Python solution on pbinfo.ro is algorithmically correct but too slow for the judge's tight time limits, this skill enables a final-resort technique: embed compiled x86-64 machine code as a bytes literal in the Python source, then load it into executable memory via mmap + ctypes and call it as if it were a normal C function.

This bypasses CPython's interpreter overhead entirely for the hot path. It has consistently moved Python solutions from 35–65 points to 100 points on problems like bunrau, xk, bitmove, and divq.

When to use this skill

Only after the general competitive-programming-solver skill's Python techniques (bulk read, array.array, slice-assign sieves, ASCII digit-parity trick, linear sieves) have been tried and still TLE. Shellcode JIT has a setup cost — don't reach for it on easy problems.

Use shellcode when:

• pure Python stdlib version of an N=10^6 algorithm runs in ~2–3+ seconds locally (will TLE on judge)
• the algorithm is a tight inner loop: nested sieves, sliding window with while, BFS over millions of edges, DP over a grid
• the user has explicitly run out of options ("am încercat tot, tot pică")
• the user has confirmed (via bunrau_diag6.py or prior experience) that pbinfo's sandbox allows PROT_EXEC on mmap pages

Don't use shellcode when:

• the problem fits in pure Python with the right algorithm
• the user is targeting a judge other than pbinfo (most judges' sandboxes block PROT_EXEC)

Pbinfo sandbox capabilities (verified)

The shellcode JIT workflow

Step 1: Write the hot path in C

Step 2: Compile with these specific flags

Step 3: Verify no relocations

Step 4: Extract the .text section and symbol offsets

Step 5: Build the Python wrapper

etc.

4KB pages are usually enough; round up to the next multiple of 4096 if needed

Build function pointers using CFUNCTYPE

...

Allocate buffers in Python and pass them in