Build a Garbage Collector in C from Scratch

To integrate the Boehm garbage collector into a C++ project, clone the Boehm GC repository as a submodule, configure it with C++ support (--enable-cplusplus), malloc redirection (--enable-malloc-redirect), thread disabling (--disable-threads), static compilation (--enable-static), and position-independent code (--enable-pic), then have all classes that need garbage collection inherit from the GC class to override new/delete operators; finally link against libgc.a and libgccpp.a while adding the include path to access gc_cpp.h headers.

When implementing an interpreter, garbage collection is important because the implementation language's garbage collector handles memory management. If you implement an interpreter in a language without garbage collection (like C), you need to implement your own memory management, which is more complex and error-prone. The garbage collector in the implementation language handles the memory management for the interpreter's runtime structures, making the implementation simpler and more reliable.

Building a custom language interpreter involves: (1) writing a parser to emit an abstract syntax tree (AST), (2) implementing an AST interpreter (which is slow because it walks through data structures), (3) implementing a runtime with garbage collection, threads, and thread scheduling, and (4) implementing a virtual machine with bytecode format. Each step introduces performance challenges, and people typically complain about slowness at each stage.

Integrating garbage collectors into a language runtime is challenging because not everyone understands how they work. Small teams may lack expertise to write garbage collectors, compilers, and standard library components simultaneously. D addresses this by providing a replaceable garbage collector interface, allowing users to select different implementations based on needs. Normal applications don't need to think about garbage collection, but systems and embedded programming may require specific constraints on collection cycle duration. The compiler writer is uniquely suited to knowing target architecture details like stack layout, which the runtime can provide through simple functions rather than complex platform-specific detection logic.

Julia 1.12 implemented a pluggable garbage collector interface, allowing users to write and use custom garbage collectors. The first consumer of this interface is MMTK (Memory Management Toolkit), an externally developed Rust-based codebase implementing several traditional GC algorithms. Currently available is a non-moving IMIX GC, with moving GC under development. This feature is particularly valuable for long-running Julia processes where memory fragmentation and allocator performance become concerns.

This section details the stop-the-world garbage collection implementation. The GC uses stop-the-world, parallel mark and sweep: all application threads stop except the GC orchestrator, roots are marked from stacks and globals, then unmarked allocations are freed. Thread suspension uses SIGPWR and SIGXCPU signals with busy state handling to avoid suspending during critical operations. The trampoline technique hijacks pthread_create using dlsym to find the real function. The parallel mark phase creates one worker thread per CPU core, each with its own worklist accessing a shared worklist. Workers scan for pointers, filtering false positives by checking heap address ranges before extent map lookup. Small allocations go to worker worklists, large allocations to shared worklists, providing good fan-out for graphs with high branching factor. The sweep phase iterates through arenas, page fillers, and blocks, freeing unmarked allocations and purging unused pages.

Stop the World garbage collection pauses the entire application to perform garbage collection work. During this pause, all application threads are suspended while the collector runs. This allows the collector to focus entirely on its work without interference. Java's Serial and Parallel GCs are Stop the World collectors. Serial GC uses a single thread for all collection work, while Parallel GC uses multiple threads for parallel marking and sweeping. Both use generation-based collection with young and old generations. Young generation uses copying collection with Eden and Survivor spaces, while old generation uses Mark and Compact. Full GC (collecting both generations) uses Mark and Compact.

Traditional garbage collectors in Java, Go, C#, and JavaScript use stop-the-world pauses to safely scan memory and reclaim garbage. While invisible for small programs, these pauses become problematic at scale: user interfaces stutter during scrolling, real-time systems miss deadlines, and large servers experience pauses that grow with heap size, sometimes lasting hundreds of milliseconds or seconds. The fundamental challenge is that programs continuously modify their memory structure while collectors scan it—new objects appear, references vanish, and pointers change mid-scan. Moving objects while programs hold references to old addresses requires atomic updates to all references, making concurrent collection one of the hardest concurrency problems in systems programming.

Concurrent garbage collection runs alongside application threads to reduce pause times. The C4 garbage collector performs continuous compaction without stop-the-world pauses. Read barriers ensure application threads see the latest relocated object locations, enabling concurrent collection. When an object is moved, read barriers force bounded copying. Compare-and-swap operations resolve conflicts when both application and GC threads attempt relocation. This approach eliminates the stop-the-world pauses that plague traditional generational collectors, essential for large-scale server applications with hundreds of gigabytes of memory.

Traditional stop-the-world garbage collection halts all application threads to perform GC work, causing visible pauses. JDK 10 introduced thread-local handshakes, allowing GC threads to communicate with individual mutator threads. Each thread independently reaches a safe point, enabling concurrent GC work while minimizing application pause times. This represents a fundamental shift from global synchronization to per-thread coordination.

An alternative to owning allocators is making them non-owning wrappers around externally managed resources. With all propagation traits set to false (the default for local allocators), allocators become bound to their containers like cats in houses. This simplifies lifetime management. An arena is a local chunk of memory with a fixed buffer and current position pointer. Arenas are not copyable to prevent duplicate ownership of the same raw memory. Allocation strategy: use buffer space if available, else fall back to global operator new. Only heap-allocated memory is deallocated—the buffer releases automatically. This enables efficient small object allocation without the complexity of state propagation.

Traditional memory management using stack and heap allocations creates complex hierarchies that are difficult to track and prone to human error. Modern operating systems virtualize memory per process, allowing programmers to think in terms of virtual linear address space rather than strict stack/heap divisions. For experienced programmers who know allocation lifetimes and sizes, arena allocators provide a better approach by grouping allocations into lifetimes. This involves asking the OS for larger memory chunks and manually slicing them for individual allocations, reducing the number of allocations from thousands to dozens.

Arena allocators extend the stack-like allocation model to larger lifetime scopes. Like the stack, arenas organize memory such that all allocations within a scope are automatically freed when that scope ends. This eliminates manual free() calls and reduces memory management complexity. Unlike the stack, arenas can be created and destroyed at arbitrary points in code, not just tied to function returns. The core principle is that memory allocation should be organized around logical lifetimes rather than function call boundaries.

A hierarchical memory pool architecture combines multiple allocation strategies. Each object has its own allocator, and when objects are removed, their pools can be freed while maintaining the property that freeing a parent pool also frees all child pools. This provides both granular control and bulk cleanup capabilities. The architecture addresses the limitation of arena allocators with growable arrays by allowing individual pool management while still enabling efficient bulk cleanup.

The arena allocator pattern simplifies manual memory management by grouping related allocations into named memory pools with unified lifetimes, reducing the complexity of tracking individual allocation lifetimes and eliminating common memory bugs like use-after-free and double-free while maintaining performance comparable to or better than general-purpose allocators.

Valgrind is a powerful profiling tool for C/C++ programs that provides two main analysis capabilities: runtime analysis using the Callgrind tool (which counts machine code instructions to measure CPU time spent in each function) and memory analysis using the Massif tool (which tracks dynamically allocated memory and identifies memory leaks). To use Valgrind, programs must be compiled with the -g flag, then executed with valgrind --tool=callgrind or valgrind --tool=massif, producing data files that are analyzed using callgrind_annotate or ms_print respectively. The analysis reveals which functions consume the most resources, helping developers identify performance bottlenecks and optimize their code.

This video demonstrates how to use Valgrind profiling tools (specifically Callgrind and KCachegrind) to identify performance bottlenecks in a custom programming language implementation, showing how profiling-guided optimization can reduce garbage collection overhead and improve execution speed by eliminating unnecessary stack operations such as redundant pops and pushes.

Valgrind is a platform with multiple tools (memcheck, cachegrind, callgrind) that instrument programs at runtime without recompilation. It detects memory errors like uninitialized access, which is valuable for finding subtle bugs that customers report as being caused by third-party libraries. Valgrind can combine with GDB via vgdb mode. Compiler sanitizers (AddressSanitizer, ThreadSanitizer) are built into the compiler, much faster (2x vs 10-100x overhead), and find overlapping but not identical bugs. They require different compilation flags for GCC vs clang.

PerfView is the primary tool for GC performance investigation. Key metrics include: (1) Time paused for GC (upper limit on achievable performance), (2) Total allocation rate (high rates indicate optimization opportunities), (3) GC heap size (identifies if GC causes large memory footprints), (4) Generation statistics (reveals allocation patterns and survival rates). Analysis should consider mean pause times (sub-10ms is typically acceptable), maximum pause times, and when spikes occur (ramp-up periods often show larger pauses). Understanding these metrics helps identify whether GC is the actual performance bottleneck.

This section demonstrates the practical impact of logging performance issues through benchmarking and memory profiling. Benchmarks reveal that calling logging methods when output is ignored takes 14 nanoseconds versus 3 nanoseconds for the conditional approach. More significantly, when parameters are involved, the non-conditional approach allocates 88 bytes due to boxing and array allocation, while the conditional approach allocates nothing. Real-world memory profiling with dotMemory shows that a simple console application logging random numbers allocated 3.6 gigabytes of memory during execution, even though nothing was printed. This demonstrates that logging performance issues scale significantly with application throughput.