for diagnosing contention on synchronization primitives in your code. Let's consider this simple workload: using Base.Threads using Profile using PProf ch = Channel(1) const N_SPAWNED_TASKS = (1 << there is indeed an excessive number of waiters in ch.CHAPTER 33. PROFILING 440 A Compute-Bound Workload Despite the wall-time profiler sampling all live tasks in the system and not just the currently precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache

for diagnosing contention on synchronization primitives in your code. Let's consider this simple workload: using Base.Threads using Profile using PProf ch = Channel(1) const N_SPAWNED_TASKS = (1 << there is indeed an excessive number of waiters in ch.CHAPTER 33. PROFILING 441 A Compute-Bound Workload Despite the wall-time profiler sampling all live tasks in the system and not just the currently precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache

for diagnosing contention on synchronization primitives in your code. Let's consider this simple workload: using Base.Threads using Profile using PProf ch = Channel(1) const N_SPAWNED_TASKS = (1 << there is indeed an excessive number of waiters in ch.CHAPTER 33. PROFILING 440 A Compute-Bound Workload Despite the wall-time profiler sampling all live tasks in the system and not just the currently precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache

for diagnosing contention on synchronization primitives in your code. Let's consider this simple workload: using Base.Threads using Profile using PProf ch = Channel(1) const N_SPAWNED_TASKS = (1 << there is indeed an excessive number of waiters in ch.CHAPTER 33. PROFILING 440 A Compute-Bound Workload Despite the wall-time profiler sampling all live tasks in the system and not just the currently precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache

for diagnosing contention on synchronization primitives in your code. Let's consider this simple workload: using Base.Threads using Profile using PProf ch = Channel(1) const N_SPAWNED_TASKS = (1 << there is indeed an excessive number of waiters in ch.CHAPTER 33. PROFILING 440 A Compute-Bound Workload Despite the wall-time profiler sampling all live tasks in the system and not just the currently precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache

precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache executes iterations dynamically to available worker threads. Current implemen- tation assumes that the workload for each iteration is uniform. However, this assumption may be removed in the future. This scheduling interface is required (no indexing). This scheduling option is generally a good choice if the workload of individual iterations is not uni- form/has a large spread. Julia 1.11 The :greedy option for

precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache executes iterations dynamically to available worker threads. Current implemen- tation assumes that the workload for each iteration is uniform. However, this assumption may be removed in the future. This scheduling interface is required (no indexing). This scheduling option is generally a good choice if the workload of individual iterations is not uni- form/has a large spread. Julia 1.11 The :greedy option for

precompile package code effectively, it's recommended to use PrecompileTools.jl to run a "precompile workload" during precom- pilation time that is representative of typical package usage, which will cache executes iterations dynamically to available worker threads. Current implemen- tation assumes that the workload for each iteration is uniform. However, this assumption may be removed in the future. This scheduling interface is required (no indexing). This scheduling option is generally a good choice if the workload of individual iterations is not uni- form/has a large spread. Julia 1.11 The :greedy option for

executes iterations dynamically to available worker threads. Current implemen- tation assumes that the workload for each iteration is uniform. However, this assumption may be removed in the future. This scheduling mechanism, this function will return nothing. On classic UNIX systems (excluding macOS), root certificates are typically stored in a file in /etc: the common places for the current UNIX system will be returned; if none of these typical root certificate paths exist, then the path to the set of root certificates that are bundled with Julia is returned. The default value returned by ca_roots() may be overridden

executes iterations dynamically to available worker threads. Current implemen- tation assumes that the workload for each iteration is uniform. However, this assumption may be removed in the future. This scheduling mechanism, this function will return nothing. On classic UNIX systems (excluding macOS), root certificates are typically stored in a file in /etc: the common places for the current UNIX system will be returned; if none of these typical root certificate paths exist, then the path to the set of root certificates that are bundled with Julia is returned. The default value returned by ca_roots() may be overridden