Cat On A Spreadsheet

Deep Profiling and Benchmarking VBA Code

Performance problems in Excel applications are often diagnosed by intuition. A macro feels slow, a recalculation seems heavy, or a report takes longer than it used to. Developers respond by guessing, rewriting sections of code, or blaming Excel itself. This approach rarely fixes the real problem and often introduces new ones.

Deep profiling replaces guesswork with evidence. By measuring execution time deliberately and consistently, VBA developers can identify true bottlenecks, quantify improvements, and prevent regressions. While VBA does not provide built-in profilers, it offers more than enough primitives to build precise and reliable measurement tools.

Why Perceived Slowness Is Misleading

Human perception is a poor performance metric. A delay caused by screen repainting feels worse than a calculation that consumes more CPU time. A one-time initialization cost feels larger than repeated micro-delays. Without measurement, optimization efforts frequently target the wrong layer.

Profiling forces clarity. It answers simple but critical questions: what actually takes time, how often it runs, and whether changes meaningfully improve execution.

Establishing a High-Resolution Timer

The foundation of profiling in VBA is a reliable timer. The built-in Timer function is sufficient for many scenarios, but higher-resolution timing is possible using the Windows performance counter.

  Private Declare PtrSafe Function QueryPerformanceCounter Lib "kernel32" (ByRef lpPerformanceCount As Currency) As Long
 
 Private Declare PtrSafe Function QueryPerformanceFrequency Lib "kernel32" (ByRef lpFrequency As Currency) As Long
 
 Public Function GetHighResTime() As Double
 
      Static freq As Currency
 
      Dim count As Currency
 
      If freq = 0 Then QueryPerformanceFrequency freq
 
      QueryPerformanceCounter count
 
      GetHighResTime = count / freq
 
 End Function

This function allows measurements precise enough to compare even small optimizations.

Timing Code Intentionally

Profiling should be explicit. Surround the code you want to measure, capture start and end times, and record the difference.

  Public Sub ProfileExample()
 
      Dim startTime As Double
 
      Dim elapsed As Double
 
      startTime = GetHighResTime()
 
      Call HeavyRoutine
 
      elapsed = GetHighResTime() - startTime
 
      Debug.Print "HeavyRoutine elapsed: " & elapsed
 
 End Sub

This pattern scales naturally as measurements become more granular.

Profiling Call Trees Instead of Single Procedures

Single measurements rarely tell the whole story. Real performance problems often hide in deep call chains where a small inefficiency is executed thousands of times. Instrumenting entry and exit points across a call tree exposes cumulative cost. Logging procedure names with timestamps creates a trace that reveals unexpected hotspots. Over time, these traces form a performance map of the system rather than isolated anecdotes.

Measuring Excel-Specific Bottlenecks

In VBA, performance is frequently dominated not by computation but by interaction with Excel itself. Reading and writing cell-by-cell, triggering recalculation repeatedly, or allowing screen updates during loops all introduce disproportionate cost.

Profiling helps distinguish pure VBA time from Excel interaction time. A routine that runs instantly when working on arrays but slows dramatically when touching ranges is providing a clear signal about where optimization should occur.

Benchmarking Alternative Implementations

Benchmarking is comparative profiling. Instead of asking whether code is slow, you ask which of two approaches is faster under realistic conditions.

  Public Sub BenchmarkAlternatives()
 
      Dim t1 As Double
 
      Dim t2 As Double
 
      t1 = GetHighResTime()
 
      Call ImplementationA
 
      t1 = GetHighResTime() - t1
 
      t2 = GetHighResTime()
 
      Call ImplementationB
 
      t2 = GetHighResTime() - t2
 
      Debug.Print "A=" & t1 & " B=" & t2
 
 End Sub

Running benchmarks multiple times and averaging results reduces noise and reveals stable trends.

Avoiding False Optimizations

Profiling often reveals that the slowest code is not where effort was originally focused. It also reveals when optimizations are meaningless. Improving a routine that accounts for two percent of runtime does not matter if eighty percent lies elsewhere. Deep profiling prevents premature optimization and channels effort toward changes that produce visible improvements.

Logging Performance Over Time

Performance regressions rarely appear overnight. They creep in through small changes. By logging benchmark results across versions, teams gain historical insight into how the system evolves. When a release suddenly feels slower, data replaces speculation. The regression can be traced, isolated, and corrected with confidence.

Profiling as a Development Habit

The most effective VBA teams treat profiling as a habit rather than a rescue tool. New features are measured. Refactors are benchmarked. Performance becomes an attribute that is observed, not assumed. This discipline transforms performance from a mystery into a managed property of the system.

Evidence-Driven Optimization

Deep profiling does not make VBA faster by itself. It makes developers smarter about where speed is lost and gained. In an environment as stateful and interaction-heavy as Excel, this clarity is essential. By replacing intuition with measurement, VBA applications can scale further, run longer, and age more gracefully — even under growing data and user expectations.