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Informational 16 Bitcoin

coins: fetch inputs in parallel

Public commit record

What the developer wrote

Authored by Andrew Toth

68/100 · Adequate
coins: fetch inputs in parallel

Leverages the thread pool to fetch inputs on multiple threads, while the overlay
serves inputs on the main thread.

This is a performance improvement over blocking the main thread to fetch inputs.

Co-authored-by: l0rinc <pap.lorinc@gmail.com>
✓ Descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context
The short version

What changed, and why it matters

This commit changes how Bitcoin Core fetches transaction input data (the 'coins' spent by transactions in a block). Instead of doing all lookups one-by-one on the main thread, it now submits the work to a thread pool. The change is described by its authors as a performance improvement only. There are no obvious security bugs in the diff, but it adds new multi-threaded state and fallback paths, which increases complexity slightly.

Recommended action

Treat as a routine performance refactor with no immediate security action required. Reviewers should verify that the work-stealing loop, StopFetching barrier, and Flush/ destructor ordering are free of data races, use-after-free, or deadlock under thread-pool stop/interrupt scenarios. Fuzz/stress test the fallback path where Submit returns nullopt.

Security signals we found

01

New concurrent shared state (m_inputs, m_input_head, m_input_tail, m_futures) across multiple worker threads and the main thread

02

Fallback path on thread-pool submission failure clears shared state and logs a warning

03

Addition of destructor and Flush override that must correctly synchronize with running workers

04

Use of relaxed memory order for m_input_head and atomic work-stealing loop

05

Asserts/Assume used for invariant checking rather than runtime error handling

Risk score

Why this scored 16/100

Our methodology →
Potential impact 2/30
Exploitability 1/25
Stealth signal 1/15
Affected reach 2/15
Confidence 7/10
Evidence quality 3/5
Human-validated context

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