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A single benchmark result is evidence about one run under one set of conditions—not a dependable measure of how a system performs in every situation. Cold-start and warmed performance can differ, and repeated runs can vary even after warm-up. To make a performance claim useful, specify the workload and system state, repeat the measurement, and show the spread as well as an appropriate summary.
Why does a single trial mislead performance claims?
A benchmark measures a system in a particular state. One run cannot show whether its result is typical, whether the same result would recur, or whether it represents the way people actually use the software. A run may begin with empty or populated caches, different device or GPU clocks, or different thermal and power conditions. Those factors can change the measured result without any change to the software being compared.
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That makes a bare claim such as “Tool A is faster” incomplete. A useful comparison needs to establish which task was measured, under what conditions, and how much results varied across repeated trials. NIST Technical Note 1830, published April 28, 2014, describes repeatability, comparability, and verifiability as dependent on trustworthy measurement and reporting procedures: NIST Technical Note 1830.
What is the difference between cold-start and warm-start performance?
“Cold” and “warm” describe different starting conditions, not two rival answers to which system is faster. A cold-start test can reveal first-use delay; a warmed test can describe repeated work after some system state has changed. Which matters depends on the task: opening an app once is not the same experience as processing a sustained stream of requests.
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Cold and warm starts on Android
Android uses specific definitions for app startup. A cold start begins when the app is not present in system memory. A warm start occurs when its process is already running in the background but the user interface must be reinitialized or brought forward. Android also distinguishes a hot start, where the activity is already cached and can return more quickly. These labels are Android app-startup categories, not universal definitions for all benchmarks. See Android’s app performance guidance.
Android’s published startup goals are under 500 ms for a cold start, under 200 ms for a warm start, and under 150 ms for a hot start. These are platform guidance targets, not guaranteed results or pass/fail thresholds for other kinds of performance tests.
“Cold” must be defined for the test
Restarting a process does not necessarily make every relevant part of a system cold. It does not, by itself, establish that operating-system caches are empty, a device has cooled down, or an app is being launched for the first time. Report what was restarted or cleared and what was left unchanged. Otherwise, readers cannot tell what the cold-start result represents.
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Warm-up runs work before measurement begins, allowing state changes such as caching effects to occur before timed results are collected. Google Benchmark notes that warm-up may be necessary for stable results because of effects such as caching. Its guide sets the default warm-up phase to zero seconds, so warm-up is disabled unless configured. The framework also supports repeated runs and statistical reporting; see the Google Benchmark User Guide.
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Warm-up is a policy, not proof that the system has reached a stable peak. In a 2016 study, Edd Barrett and coauthors found that at most 43.5% of the studied VM-benchmark pairs consistently reached a steady state of peak performance across three machines. That finding applies to the VMs and benchmarks in that study, not to all software or hardware. The paper is available at “Virtual Machine Warmup Blows Hot and Cold”.
Consequently, a fixed warm-up duration should not be treated as a universal guarantee. State how warm-up was handled, and, when claiming steady-state performance, explain what evidence supports that label. For further context on Java benchmark steady-state assessment, see Traini and coauthors’ 2022 study, “Towards effective assessment of steady state performance in Java software: Are we there yet?”.
How many benchmark runs are enough?
There is no universal run count established by these sources. The number needed depends on the workload and the variation observed. A workload with noisy results needs more evidence to characterize its spread than one whose repeated measurements are tightly clustered. Reporting one observation as though it were precise hides that uncertainty.
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What should a benchmark report include?
Make the protocol clear enough that another reader can understand what the number means and compare like with like. Include:
- Target task and metric: identify the workload and whether the claim concerns first-use latency, sustained throughput, or another outcome.
- Starting state: say whether the measurement is cold, warm, or intended to represent steady state, and describe how that condition was produced.
- Warm-up policy: report whether warm-up was used and how it was configured. Do not imply that warm-up automatically established a stable peak.
- Repetitions and variation: give the number of measured runs and show an appropriate summary and spread, preferably with enough run-level information to reveal outliers.
- Environment: record machine and software versions, workload details, and relevant power or thermal conditions. Google Benchmark includes machine information in its report preamble.
- Comparison protocol: keep the workload and measurement conditions consistent between the systems being compared, and disclose any differences.
Why can repeated GPU runs change over time?
GPU matrix multiplication is one example of why measurement conditions matter. NVIDIA’s CUTLASS guidance discusses transient cache, memory-page, clock, power, and thermal conditions for its GEMM benchmark context, and recommends controls such as warm-up, profiling, cool-down, and attention to cache residency. These are context-specific recommendations, not a universal recipe for every software benchmark.
In the described setup, NVIDIA observed GPC clock oscillation for around the first three seconds of back-to-back GEMMs before the clocks settled. The same guidance says that previous GEMM studies found more than 1,000 testing iterations provided stable results for large problem sizes around 4096×4096×4096, while smaller problems may require more iterations. Neither the roughly three-second observation nor that iteration count should be transferred uncritically to other hardware, workloads, or benchmark designs. See NVIDIA CUTLASS GEMM Performance Measurement Methodology Guidelines.
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Before treating a result as evidence that one system is faster, check whether the claims match on the points that determine what was measured:
- Are both results cold-start, warm-start, or warmed/steady-state measurements?
- Do they use the same workload, metric, and relevant environment?
- How many measured repetitions are reported, and how is variation shown?
- Is warm-up configured, and is there evidence that the measured state is stable?
- Does the chosen metric reflect the reader’s actual task, such as first-use latency or sustained throughput?
If a result omits these details, it may still describe one observed run, but it cannot establish that the number is repeatable or representative of a different starting state or use case.
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