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Profiling Command Line Tools

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 250 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     ||             2150ms  86.0% ||             2150ms  86.0% || primes.js~13-22:191-419
     next       ||             2470ms  98.8% ||              320ms  12.8% || primes.js~31-37:537-737
     :program   ||             2500ms 100.0% ||               30ms   1.2% || primes.js~1-46:0-982
    ----------------------------------------------------------------------------------------------
   

   By default the sampler prints an execution time histogram for each JavaScript function.

   You can produce a flame graph in SVG format by requesting that with the --cpusampler=flamegraph option:

    js primes.js --cpusampler=flamegraph
   

   It should produce a file called flamegraph.svg containing 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 468336895 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   |     234117338  50.0% |        365660   0.2% |     233751678  99.8% | primes.js~15:245-258
     accept   |     117053670  25.0% |        182582   0.2% |     116871088  99.8% | primes.js~16-18:275-348
     accept   |     117005061  25.0% |        181001   0.2% |     116824060  99.8% | primes.js~19:362-381
     accept   |         53608   0.0% |          1829   3.4% |         51779  96.6% | primes.js~14:211-227
     accept   |         53608   0.0% |          1829   3.4% |         51779  96.6% | primes.js~13-22:191-419
     accept   |         48609   0.0% |          1581   3.3% |         47028  96.7% | primes.js~17:322-334
     accept   |          4999   0.0% |           248   5.0% |          4751  95.0% | primes.js~21:402-413
     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~3:45-55
    -----------------------------------------------------------------------------------------
   

   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 5007 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  99.9% |     5000  99.9% | primes.js~31-37:537-737
    :program |        6   0.1% |     5007 100.0% | primes.js~1-46:0-982
    Primes   |        1   0.0% |        1   0.0% | primes.js~25-38:424-739
   --------------------------------------------------------
   

   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.

Tools Options #

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.

CPU Sampler Options #

• --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.FilterMimeType=<String>: profiles languages with mime-type. There is no filter by default.
• --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.MinSamples=<Integer>: removes elements from output if they have less samples than this value (default is 0).
• --cpusampler.Output=<Output>: prints a histogram, calltree, json, or flamegraph as output. The default is histogram.
• --cpusampler.OutputFile=<String>: saves output to the given file. Output is printed to output stream by default.
• --cpusampler.Period=<Long>: specifies the period, in milliseconds, to sample the stack.
• --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.

CPU Tracer Options #

• --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.FilterMimeType=<String>: profiles languages with mime-type. There is no filter by default.
• --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.
• --cpusampler.OutputFile=<String>: saves output to the given file. Output is printed to output stream by default.
• --cputracer.TraceCalls: captures calls when tracing. 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.

Memory Tracer Options #

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.FilterMimeType=<String>: profiles languages with mime-type. There is no filter by default.
• --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.TraceRoots=<Boolean>: captures roots when tracing. The default is true.
• --memtracer.TraceStatements: captures statements when tracing. The default is false.