Docs: add GC user docs

doc/make.jl

@@ -192,6 +192,7 @@ Manual = [
     "manual/code-loading.md",
     "manual/profile.md",
     "manual/stacktraces.md",
+    "manual/memory-management.md",
     "manual/performance-tips.md",
     "manual/workflow-tips.md",
     "manual/style-guide.md",

doc/src/manual/command-line-interface.md

@@ -180,7 +180,7 @@ The following is a complete list of command-line switches available when launchi
 |`-m`, `--module <Package> [args]`      |Run entry point of `Package` (`@main` function) with `args`|
 |`-L`, `--load <file>`                  |Load `<file>` immediately on all processors|
 |`-t`, `--threads {auto\|N[,auto\|M]}`  |Enable N[+M] threads; N threads are assigned to the `default` threadpool, and if M is specified, M threads are assigned to the `interactive` threadpool; `auto` tries to infer a useful default number of threads to use but the exact behavior might change in the future. Currently sets N to the number of CPUs assigned to this Julia process based on the OS-specific affinity assignment interface if supported (Linux and Windows) or to the number of CPU threads if not supported (MacOS) or if process affinity is not configured, and sets M to 1.|
-| `--gcthreads=N[,M]`                   |Use N threads for the mark phase of GC and M (0 or 1) threads for the concurrent sweeping phase of GC. N is set to the number of compute threads and M is set to 0 if unspecified.|
+| `--gcthreads=N[,M]`                   |Use N threads for the mark phase of GC and M (0 or 1) threads for the concurrent sweeping phase of GC. N is set to the number of compute threads and M is set to 0 if unspecified. See [Memory Management and Garbage Collection](@ref man-memory-management) for more details.|
 |`-p`, `--procs {N\|auto}`              |Integer value N launches N additional local worker processes; `auto` launches as many workers as the number of local CPU threads (logical cores)|
 |`--machine-file <file>`                |Run processes on hosts listed in `<file>`|
 |`-i`, `--interactive`                  |Interactive mode; REPL runs and `isinteractive()` is true|
@@ -206,7 +206,7 @@ The following is a complete list of command-line switches available when launchi
 |`--track-allocation=@<path>`           |Count bytes but only in files that fall under the given file path/directory. The `@` prefix is required to select this option. A `@` with no path will track the current directory.|
 |`--task-metrics={yes\|no*}`             |Enable the collection of per-task metrics|
 |`--bug-report=KIND`                    |Launch a bug report session. It can be used to start a REPL, run a script, or evaluate expressions. It first tries to use BugReporting.jl installed in current environment and falls back to the latest compatible BugReporting.jl if not. For more information, see `--bug-report=help`.|
-|`--heap-size-hint=<size>`              |Forces garbage collection if memory usage is higher than the given value. The value may be specified as a number of bytes, optionally in units of KB, MB, GB, or TB, or as a percentage of physical memory with %.|
+|`--heap-size-hint=<size>`              |Forces garbage collection if memory usage is higher than the given value. The value may be specified as a number of bytes, optionally in units of KB, MB, GB, or TB, or as a percentage of physical memory with %. See [Memory Management and Garbage Collection](@ref man-memory-management) for more details.|
 |`--compile={yes*\|no\|all\|min}`       |Enable or disable JIT compiler, or request exhaustive or minimal compilation|
 |`--output-o <name>`                    |Generate an object file (including system image data)|
 |`--output-ji <name>`                   |Generate a system image data file (.ji)|

doc/src/manual/memory-management.md

@@ -0,0 +1,177 @@
+# [Memory Management and Garbage Collection](@id man-memory-management)
+
+Julia uses automatic memory management through its built-in garbage collector (GC). This section provides an overview of how Julia manages memory and how you can configure and optimize memory usage for your applications.
+
+## [Garbage Collection Overview](@id man-gc-overview)
+
+Julia features a garbage collector with the following characteristics:
+
+* **Non-moving**: Objects are not relocated in memory during garbage collection
+* **Generational**: Younger objects are collected more frequently than older ones
+* **Parallel and partially concurrent**: The GC can use multiple threads and run concurrently with your program
+* **Mostly precise**: The GC accurately identifies object references for pure Julia code, and it provides conservative scanning APIs for users calling Julia from C
+
+The garbage collector automatically reclaims memory used by objects that are no longer reachable from your program, freeing you from manual memory management in most cases.
+
+## [Memory Architecture](@id man-memory-architecture)
+
+Julia uses a two-tier allocation strategy:
+
+* **Small objects** (currently ≤ 2032 bytes but may change): Allocated using a fast per-thread pool allocator
+* **Large objects** : Allocated directly through the system's `malloc`
+
+This hybrid approach optimizes for both allocation speed and memory efficiency, with the pool allocator providing fast allocation for the many small objects typical in Julia programs.
+
+## [System Memory Requirements](@id man-system-memory)
+
+### Swap Space
+
+Julia's garbage collector is designed with the expectation that your system has adequate swap space configured. The GC uses heuristics that assume it can allocate memory beyond physical RAM when needed, relying on the operating system's virtual memory management.
+
+If your system has limited or no swap space, you may experience out-of-memory errors during garbage collection. In such cases, you can use the `--heap-size-hint` option to limit Julia's memory usage.
+
+### Memory Hints
+
+You can provide a hint to Julia about the maximum amount of memory to use:
+
+```bash
+julia --heap-size-hint=4G  # To set the hint to ~4GB
+julia --heap-size-hint=50% # or to 50% of physical memory
+```
+
+The `--heap-size-hint` option tells the garbage collector to trigger collection more aggressively when approaching the specified limit. This is particularly useful in:
+
+* Containers with memory limits
+* Systems without swap space
+* Shared systems where you want to limit Julia's memory footprint
+
+You can also set this via the `JULIA_HEAP_SIZE_HINT` environment variable:
+
+```bash
+export JULIA_HEAP_SIZE_HINT=2G
+julia
+```
+
+## [Multithreaded Garbage Collection](@id man-gc-multithreading)
+
+Julia's garbage collector can leverage multiple threads to improve performance on multi-core systems.
+
+### GC Thread Configuration
+
+By default, Julia uses multiple threads for garbage collection:
+
+* **Mark threads**: Used during the mark phase to trace object references (default: 1, which is shared with the compute thread if there is only one, otherwise half the number of compute threads)
+* **Sweep threads**: Used for concurrent sweeping of freed memory (default: 0, disabled)
+
+You can configure GC threading using:
+
+```bash
+julia --gcthreads=4,1  # 4 mark threads, 1 sweep thread
+julia --gcthreads=8    # 8 mark threads, 0 sweep threads
+```
+
+Or via environment variable:
+
+```bash
+export JULIA_NUM_GC_THREADS=4,1
+julia
+```
+
+### Recommendations
+
+For compute-intensive workloads:
+
+* Use multiple mark threads (the default configuration is usually appropriate)
+* Consider enabling concurrent sweeping with 1 sweep thread for allocation-heavy workloads
+
+For memory-intensive workloads:
+
+* Enable concurrent sweeping to reduce GC pauses
+* Monitor GC time using `@time` and adjust thread counts accordingly
+
+## [Monitoring and Debugging](@id man-gc-monitoring)
+
+### Basic Memory Monitoring
+
+Use the `@time` macro to see memory allocation and GC overhead:
+
+```julia
+julia> @time some_computation()
+  2.123456 seconds (1.50 M allocations: 58.725 MiB, 17.17% gc time)
+```
+
+### GC Logging
+
+Enable detailed GC logging to understand collection patterns:
+
+```julia
+julia> GC.enable_logging(true)
+julia> # Run your code
+julia> GC.enable_logging(false)
+```
+
+This logs each garbage collection event with timing and memory statistics.
+
+### Manual GC Control
+
+While generally not recommended, you can manually trigger garbage collection:
+
+```julia
+GC.gc()          # Force a garbage collection
+GC.enable(false) # Disable automatic GC (use with caution!)
+GC.enable(true)  # Re-enable automatic GC
+```
+
+**Warning**: Disabling GC can lead to memory exhaustion. Only use this for specific performance measurements or debugging.
+
+## [Performance Considerations](@id man-gc-performance)
+
+### Reducing Allocations
+
+The best way to minimize GC impact is to reduce unnecessary allocations:
+
+* Use in-place operations when possible (e.g., `x .+= y` instead of `x = x + y`)
+* Pre-allocate arrays and reuse them
+* Avoid creating temporary objects in tight loops
+* Consider using `StaticArrays.jl` for small, fixed-size arrays
+
+### Memory-Efficient Patterns
+
+* Avoid global variables that change type
+* Use `const` for global constants
+
+### Profiling Memory Usage
+
+For detailed guidance on profiling memory allocations and identifying performance bottlenecks, see the [Profiling](@ref man-profiling) section.
+
+## [Advanced Configuration](@id man-gc-advanced)
+
+### Integration with System Memory Management
+
+Julia works best when:
+
+* The system has adequate swap space (recommended: 2x physical RAM)
+* Virtual memory is properly configured
+* Other processes leave sufficient memory available
+* Container memory limits are set appropriately with `--heap-size-hint`
+
+## [Troubleshooting Memory Issues](@id man-gc-troubleshooting)
+
+### High GC Overhead
+
+If garbage collection is taking too much time:
+
+1. **Reduce allocation rate**: Focus on algorithmic improvements
+2. **Adjust GC threads**: Experiment with different `--gcthreads` settings
+3. **Use concurrent sweeping**: Enable background sweeping with `--gcthreads=N,1`
+4. **Profile memory patterns**: Identify allocation hotspots and optimize them
+
+### Memory Leaks
+
+While Julia's GC prevents most memory leaks, issues can still occur:
+
+* **Global references**: Avoid holding references to large objects in global variables
+* **Closures**: Be careful with closures that capture large amounts of data
+* **C interop**: Ensure proper cleanup when interfacing with C libraries
+
+For more detailed information about Julia's garbage collector internals, see the Garbage Collection section in the Developer Documentation.

doc/src/manual/multi-threading.md

@@ -84,13 +84,15 @@ julia> Threads.threadid()
 
 ### Multiple GC Threads
 
-The Garbage Collector (GC) can use multiple threads. The amount used is either half the number
-of compute worker threads or configured by either the `--gcthreads` command line argument or by using the
+The Garbage Collector (GC) can use multiple threads. The amount used by default matches the compute
+worker threads or can configured by either the `--gcthreads` command line argument or by using the
 [`JULIA_NUM_GC_THREADS`](@ref JULIA_NUM_GC_THREADS) environment variable.
 
 !!! compat "Julia 1.10"
     The `--gcthreads` command line argument requires at least Julia 1.10.
 
+For more details about garbage collection configuration and performance tuning, see [Memory Management and Garbage Collection](@ref man-memory-management).
+
 ## [Threadpools](@id man-threadpools)
 
 When a program's threads are busy with many tasks to run, tasks may experience

doc/src/manual/performance-tips.md

@@ -116,6 +116,8 @@ Consequently, in addition to the allocation itself, it's very likely
 that the code generated for your function is far from optimal. Take such indications seriously
 and follow the advice below.
 
+For more information about memory management and garbage collection in Julia, see [Memory Management and Garbage Collection](@ref man-memory-management).
+
 In this particular case, the memory allocation is due to the usage of a type-unstable global variable `x`, so if we instead pass `x` as an argument to the function it no longer allocates memory
 (the remaining allocation reported below is due to running the `@time` macro in global scope)
 and is significantly faster after the first call: