Stack vs malloc: real-world benchmark shows 2–6x difference
<p>Usually, we assume that malloc is fast—and in most cases it is. <br> However, sometimes "reasonable" code can lead to very unreasonable performance.</p> <p>In a previous post, I looked at using stack-based allocation (VLA / fixed-size) for temporary data, and another on estimating available stack space to use it safely.</p> <p>This time I wanted to measure the actual impact in a realistic workload.</p> <h3> Full Article (Medium - no paywall): </h3> <p><a href="https://blog.stackademic.com/temporary-memory-isnt-free-allocation-strategies-and-their-hidden-costs-159247f7f856" rel="noopener noreferrer">Stack vs malloc: real-world benchmark shows 2–6x difference</a></p> <p>I built a benchmark based on a loan portfolio PV calculation, where each loan creates several temporary arrays (thousand
Usually, we assume that malloc is fast—and in most cases it is. However, sometimes "reasonable" code can lead to very unreasonable performance.
In a previous post, I looked at using stack-based allocation (VLA / fixed-size) for temporary data, and another on estimating available stack space to use it safely.
This time I wanted to measure the actual impact in a realistic workload.
Full Article (Medium - no paywall):
Stack vs malloc: real-world benchmark shows 2–6x difference
I built a benchmark based on a loan portfolio PV calculation, where each loan creates several temporary arrays (thousands of elements each). This is fairly typical code-clean, modular, nothing unusual.
I compared:
-
stack allocation (VLA)
-
heap per-loan (malloc/free)
-
heap reuse
-
static (baseline)
Results:
-
stack allocation stays very close to optimal
-
heap per-loan can be ~2.5x slower (glibc) and up to ~6x slower (musl)
-
even optimized allocators show pattern-dependent behavior
The main takeaway for me: allocation cost is usually hidden—but once it's in the hot path, it really matters.
Curious how others approach temporary workspace in performance-sensitive code.
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