GraalVM profiling command line tools help you optimize your code through analysis of CPU and memory usage.

Most applications spend 80% of their runtime in 20% of the code. For this reason, to optimize the code, it is essential to know where the application spends its time. In this section, we use an example application to demonstrate the three main profiling capabilities that GraalVM offers: CPU Tracer, CPU Sampler, and Memory Tracer.

This example application uses a basic prime number calculator based on the Sieve of Eratosthenes algorithm.

1. Copy the following code into a new file named primes.js:

   class AcceptFilter {
       accept(n) {
           return true
       }
   }
   
   class DivisibleByFilter {
       constructor(number, next) {
           this.number = number;
           this.next = next;
       }
   
       accept(n) {
           var filter = this;
           while (filter != null) {
               if (n % filter.number === 0) {
                   return false;
               }
               filter = filter.next;
           }
           return true;
       }
   }
   
   class Primes {
       constructor() {
           this.number = 2;
           this.filter = new AcceptFilter();
       }
   
       next() {
           while (!this.filter.accept(this.number)) {
               this.number++;
           }
           this.filter = new DivisibleByFilter(this.number, this.filter);
           return this.number;
       }
   }
   
   var primes = new Primes();
   var primesArray = [];
   for (let i = 0; i < 5000; i++) {
       primesArray.push(primes.next());
   }
   console.log(`Computed ${primesArray.length} prime numbers. ` +
               `The last 5 are ${primesArray.slice(-5)}.`);

2. Run js primes.js. The example application should print output as follows:

   js primes.js
   Computed 5000 prime numbers. The last 5 are 48563,48571,48589,48593,48611.

   This program takes a moment to compute. Next you will check where the time is spent.

3. Run js primes.js --cpusampler to enable CPU sampling. CPU Sampler should print output for the example application as follows:

   js primes.js --cpusampler
   
   Computed 5000 prime numbers. The last 5 are 48563,48571,48589,48593,48611.
   ----------------------------------------------------------------------------------------------------
   Sampling Histogram. Recorded 825 samples with period 10ms.
     Self Time: Time spent on the top of the stack.
     Total Time: Time spent somewhere on the stack.
   ----------------------------------------------------------------------------------------------------
   Thread[main,5,main]
    Name              |             Total Time    ||              Self Time    || Location             
   ----------------------------------------------------------------------------------------------------
    accept            |             7330ms  88.8% ||             7330ms  88.8% || primes.js~13-22:191-419
    :program          |             8250ms 100.0% ||              420ms   5.1% || primes.js~1-46:0-982
    next              |             7820ms  94.8% ||              250ms   3.0% || primes.js~31-37:537-737
    DivisibleByFilter |              440ms   5.3% ||              240ms   2.9% || primes.js~7-23:66-421
    AcceptFilter      |               20ms   0.2% ||               10ms   0.1% || primes.js~1-5:0-63
    Primes            |               20ms   0.2% ||                0ms   0.0% || primes.js~25-38:424-739
   ----------------------------------------------------------------------------------------------------

   By default the sampler prints an execution time histogram for each JavaScript function. You can produce a flame graph in SVG format by doing

   js primes.js --cpusampler --cpusampler.Output=flamegraph --cpusampler.OutputFile=primes.svg

   which should produce something like this

   

   You can zoom into the graph by clicking on elements.

   By default, CPU sampling takes a sample every 10 milliseconds. From the result, we can see that roughly 89% of the time is spent in the DivisibleByFilter.accept function.

   accept(n) {
       var filter = this;
       while (filter != null) {
           if (n % filter.number === 0) {
               return false;
           }
           filter = filter.next;
       }
       return true;
   }

   Now use the CPU Tracer to collect execution counts of each statement:

4. Run js primes.js --cputracer --cputracer.TraceStatements --cputracer.FilterRootName=*accept to collect execution counts for all statements in methods ending with accept:

   js primes.js --cputracer --cputracer.TraceStatements --cputracer.FilterRootName=*accept
   Computed 5000 prime numbers. The last 5 are 48563,48571,48589,48593,48611.
   -----------------------------------------------------------------------------------------
   Tracing Histogram. Counted a total of 351278226 element executions.
     Total Count: Number of times the element was executed and percentage of total executions.
     Interpreted Count: Number of times the element was interpreted and percentage of total executions of this element.
     Compiled Count: Number of times the compiled element was executed and percentage of total executions of this element.
   -----------------------------------------------------------------------------------------
    Name     |          Total Count |    Interpreted Count |       Compiled Count | Location
   -----------------------------------------------------------------------------------------
    accept   |     117058669  33.3% |         63575   0.1% |     116995094  99.9% | primes.js~15:245-258
    accept   |     117053670  33.3% |         63422   0.1% |     116990248  99.9% | primes.js~16-18:275-348
    accept   |     117005061  33.3% |         61718   0.1% |     116943343  99.9% | primes.js~19:362-381
    accept   |         53608   0.0% |          1857   3.5% |         51751  96.5% | primes.js~14:215-227
    accept   |         53608   0.0% |          1857   3.5% |         51751  96.5% | primes.js~13-22:191-419
    accept   |         48609   0.0% |          1704   3.5% |         46905  96.5% | primes.js~17:322-334
    accept   |          4999   0.0% |           153   3.1% |          4846  96.9% | primes.js~21:409-412
    accept   |             1   0.0% |             1 100.0% |             0   0.0% | primes.js~2-4:25-61
    accept   |             1   0.0% |             1 100.0% |             0   0.0% | primes.js~3:52-55
   -----------------------------------------------------------------------------------------

   The output shows execution counters for each statement, instead of timing information. Tracing histograms often provides insights into the behavior of the algorithm that needs optimization.

5. Run js primes.js --experimental-options --memtracer to display source code locations and counts of reported allocations. Note that the Memory Tracer tool for capturing allocations is currently an experimental feature in GraalVM. As such, --memtracer must be preceded by the --experimental-options command line option.

   js primes.js --experimental-options --memtracer
   Computed 5000 prime numbers. The last 5 are 48563,48571,48589,48593,48611.
   ------------------------------------------------------------
    Location Histogram with Allocation Counts. Recorded a total of 5013 allocations.
      Total Count: Number of allocations during the execution of this element.
      Self Count: Number of allocations in this element alone (excluding sub calls).
   ------------------------------------------------------------
    Name        |      Self Count |     Total Count |  Location
   ------------------------------------------------------------
    next        |     5000  99.7% |     5000  99.7% | primes.js~31-37:537-737
    :program    |       11   0.2% |     5013 100.0% | primes.js~1-46:0-966
    Primes      |        1   0.0% |        1   0.0% | primes.js~25-38:454-739
   ------------------------------------------------------------
   

   This output shows the number of allocations which were recorded per function. For each prime number that was computed, the program allocates one object in next and one in constructor of DivisibleByFilter. Allocations are recorded independently of whether they could get eliminated by the compiler.

   The GraalVM compiler is particularly powerful in optimizing allocations and can push allocations into infrequent branches to increase execution performance. The GraalVM team plans to add information about memory optimizations to the memory tracer in the future.

Use the --help:tools option in all guest language launchers to display reference information for CPU Sampler, CPU Tracer, and Memory Tracer.

The current set of available options is as follows:

• --cpusampler: enables CPU Sampler. Disabled by default.
• --cpusampler.Delay=<Long>: delays the sampling for the given number of milliseconds (default: 0).
• --cpusampler.FilterFile=<Expression>: applies a wildcard filter for source file paths, for example, *program*.sl. The default is ∗.
• --cpusampler.FilterLanguage=<String>: profiles languages only with the matching mime-type, for example, +. The default is no filter.
• --cpusampler.FilterRootName=<Expression>: applies a wildcard filter for program roots, for example, Math.*. The default is ∗.
• --cpusampler.GatherHitTimes: saves a timestamp for each taken sample. The default is false.
• --cpusampler.Mode=<Mode>: describes the level of sampling detail. Please note that increased detail can lead to reduced accuracy.
  • exclude_inlined_roots: samples roots excluding inlined functions (enabled by default)
  • roots: samples roots including inlined functions
  • statements: samples all statements
• --cpusampler.Output=<Output>: prints a histogram, calltree, json, or flamegraph as output. The default is histogram.
• --cpusampler.Period=<Long>: specifies the period, in milliseconds, to sample the stack.
• --cpusampler.SampleInternal: captures internal elements. The default is false.
• --cpusampler.StackLimit=<Integer>: specifies the maximum number of stack elements.
• --cpusampler.SummariseThreads : prints sampling output as a summary of all per thread profiles. The default is false.

• --cputracer: enables the CPU tracer. Disabled by default.
• --cputracer.FilterFile=<Expression>: applies a wildcard filter for source file paths, for example, *program*.sl. The default is ∗.
• --cputracer.FilterLanguage=<String>: profiles languages only with the matching mime-type, for example, +. The default is no filter.
• --cputracer.FilterRootName=<Expression>: applies a wildcard filter for program roots, for example, Math.*. The default is ∗.
• --cputracer.Output=<Output> prints a histogram or json as output. The default is histogram.
• --cputracer.TraceCalls: captures calls when tracing. The default is false.
• --cputracer.TraceInternal: traces internal elements. The default is false.
• --cputracer.TraceRoots=<Boolean>: captures roots when tracing. The default is true.
• --cputracer.TraceStatements: captures statements when tracing. The default is false.

The memory tracer tool is currently an experimental tool. Make sure to prepend the --experimental-options flag to enable --memtracer.

• --memtracer: enables the memory tracer. Disabled by default.
• --memtracer.FilterFile=<Expression>: applies a wildcard filter for source file paths, for example, *program*.sl. The default is ∗.
• --memtracer.FilterLanguage=<String>: profiles languages only with the matching mime-type, for example, +. The default is no filter.
• --memtracer.FilterRootName=<Expression>: applies a wildcard filter for program roots, for example, Math.*. The default is ∗.
• --memtracer.Output=<Format>: prints a typehistogram, histogram, or calltree as output. The default is histogram.
• --memtracer.StackLimit=<Integer>: sets the maximum number of maximum stack elements.
• --memtracer.TraceCalls: captures calls when tracing. The default is false.
• --memtracer.TraceInternal: captures internal elements. The default is false.
• --memtracer.TraceRoots=<Boolean>: captures roots when tracing. The default is true.
• --memtracer.TraceStatements: captures statements when tracing. The default is false.