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A single benchmark run measures one outcome under one set of conditions; it does not show how repeatable that outcome is or whether it represents first use, warmed operation, or sustained performance. Cold-start and warm-start results answer different questions, so a credible performance claim labels the workload and system state, repeats the measurement, and shows variation.
Why can warm and cold performance differ?
Running a workload can change the state in which later measurements occur. Caches may be populated, data may be resident in memory, and hardware clocks, power use, and temperature may shift. As a result, the first run and subsequent runs can measure different conditions rather than two samples of an unchanged system.
Google Benchmark notes that warming up may be necessary to obtain stable results, for example because of caching effects. Its warm-up phase runs the benchmark before measured iterations count; the documented default is zero seconds, so warm-up is disabled unless configured. That setting is a framework default, not evidence that a workload needs no warm-up. Google Benchmark User Guide
Warm-up also does not guarantee that a system has reached a stable peak. In a 2016 study across three machines, Barrett and coauthors found that at most 43.5% of the VM-benchmark pairs they studied consistently reached a steady state of peak performance. The finding applies to those tested pairs, not to all software or hardware. Barrett et al., “Virtual Machine Warmup Blows Hot and Cold”
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What do cold start and warm start mean?
These labels depend on the platform and workload. For Android app startup, a cold start means launching an app that is not present in system memory. A warm start means its process is already running in the background, but the user interface must be reinitialized or brought forward. Android calls a launch with the activity already cached a hot start. These are Android-specific categories, not universal definitions for every benchmark. Android Developers: Overview of measuring app performance
Android’s documented startup goals are under 500 ms for cold starts, under 200 ms for warm starts, and under 150 ms for hot starts. They are platform guidance targets, not guaranteed results or general thresholds for other applications, devices, and workloads. The difference between the categories illustrates why a result without its startup state is hard to interpret.
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Why is one trial not enough to support a broad claim?
It cannot reveal normal variation
A single value gives no view of how much results fluctuate across repeated runs. It might be typical, unusually fast, or unusually slow. Google Benchmark supports repeated runs and reports statistics across repetitions, allowing readers to see more than one point estimate. Google Benchmark User Guide
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If the first run is measured cold and later runs are measured after caches, clocks, or temperatures have changed, treating them as interchangeable samples obscures what was actually tested. Conversely, discarding initial runs without reporting that choice can hide first-use latency that matters to users.
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It may not represent the user’s task
First-use latency, sustained throughput, and the latency of a warmed operation are different questions. A fast warmed operation does not establish a fast first launch; a cold-start result does not establish sustained throughput. The benchmark should match the task behind the claim.
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. Repeat measurements until the spread and stability are clear enough for the claim being made, and retain run-level outcomes so the reported summary can be checked. Do not present an arbitrary count as a guarantee of statistical confidence.
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Iteration counts are especially workload-specific. NVIDIA’s CUTLASS GEMM guidance reports that more than 1,000 iterations produced stable results for large problem sizes around 4096×4096×4096, based on earlier GEMM studies; smaller problems may require more iterations. That example is for its GPU matrix-multiplication context, not a prescription for general software benchmarks. NVIDIA CUTLASS GEMM performance measurement methodology
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- Define the question. State whether the claim concerns first-use latency, warmed-operation latency, sustained throughput, or another metric. Measure and label each relevant state; report both cold and warm results when both matter.
- Describe how the state was created. Say what was restarted, cleared, preloaded, or held constant. A fresh process alone does not prove that operating-system caches are empty, that a device is cold, or that an app is being launched for the first time.
- Specify the warm-up policy. Report whether warm-up was used, how it was configured, and which iterations were excluded from measurement. Do not imply that a fixed duration establishes steady state unless the measurements support that conclusion.
- Repeat the measured workload. Report the number of repetitions and preserve enough run-level information to show spread and outliers. Choose summaries that fit the metric rather than relying on one result.
- Document the environment and protocol. Include machine and software versions, workload, relevant power and thermal conditions, and other settings that could affect results. Keep these consistent when comparing systems and disclose any differences.
- Use a distribution suited to the task. For Android startup latency, Android guidance points to median, P95, and P99. For other workloads, select appropriate summaries and show enough variation to make the result interpretable.
What do hardware-specific examples show?
Transient conditions can matter even when a benchmark repeatedly executes the same operation. NVIDIA’s CUTLASS GEMM guidance describes cache, memory-page, clock, power, and thermal effects and recommends controls such as warm-up, profiling, cool-down, and managing cache residency in its GPU matrix-multiplication context. In the described setup, GPC clocks were observed to oscillate for around the first three seconds of back-to-back GEMMs before settling. That observation is not a general warm-up duration for other workloads or hardware. NVIDIA CUTLASS GEMM performance measurement methodology
How can you compare two performance claims fairly?
- State: Are both results cold-start, warm-start, or steady-state measurements?
- Workload and environment: Are the task, machine, software, and relevant conditions comparable?
- Repetition: How many runs were measured, and how is variation represented?
- Warm-up: Was it configured, and is there evidence that the system reached the state being claimed?
- Metric: Does the result describe what users care about—such as startup latency or sustained throughput?
NIST Technical Note 1830, published April 28, 2014, frames trusted measurement and reporting procedures as necessary for repeatability, comparability, and verifiability of performance claims. NIST: “Ghost in the Machine: Don’t Let It Haunt Your Software Performance Measurements”
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