How do I use Mesh with my C++ application?

Link Mesh directly or use LD_PRELOAD to override malloc, as demonstrated in the README with 'LD_PRELOAD=libmesh.so git status'. No application code changes are needed.

Does Mesh work on Windows?

Mesh currently runs on Linux and macOS, with Windows support in progress. It's not stable for production use on Windows yet.

Mesh vs jemalloc: which is better for memory fragmentation?

Mesh excels at automatic compaction without code changes, while jemalloc focuses on scalability and fragmentation reduction through different algorithms. Choose Mesh for transparent recovery, jemalloc for multi-threaded performance.

How to monitor Mesh's memory savings?

Set MALLOCSTATS=1 when running your application; Mesh will print a summary at exit with details like meshed MB and allocation counts.

Is Mesh suitable for embedded systems?

Mesh can help in memory-constrained environments, but the compaction overhead might not be ideal for very resource-limited or real-time embedded systems. Test thoroughly for your specific workload.

How to build Mesh from source on macOS?

Clone the repository, run 'make' and 'sudo make install' as per the README. Ensure you have Bazel or the required build tools installed.

Mesh

Apache-2.0C++

A drop-in malloc replacement that automatically reduces memory fragmentation in C/C++ applications without code changes.

GitHub

1.9k stars79 forks0 contributors

What is Mesh?

Mesh is a memory allocator that automatically reduces memory fragmentation in C/C++ applications. It serves as a drop-in replacement for malloc that can transparently compact memory without requiring any changes to application code, addressing the long-standing problem of memory fragmentation in long-running programs.

Target Audience

C/C++ developers and system programmers working on applications that suffer from memory fragmentation issues, particularly those running on Linux or macOS systems who want automatic memory optimization without code modifications.

Value Proposition

Developers choose Mesh because it provides automatic memory compaction capabilities unavailable in standard allocators, requires zero code changes through its drop-in replacement approach, and integrates transparently via LD_PRELOAD while maintaining performance through its novel architecture.

Overview

A memory allocator that automatically reduces the memory footprint of C/C++ applications.

Use Cases

Best For

Reducing memory footprint in long-running C/C++ server applications

Related Projects

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Addressing memory fragmentation in applications with complex allocation patterns

Optimizing memory usage without modifying existing application code

Replacing standard malloc with automatic compaction capabilities

Monitoring memory fragmentation recovery through built-in statistics

Improving memory efficiency in Linux and macOS C/C++ programs

Not Ideal For

Real-time systems requiring predictable allocation latency
Windows-only applications due to incomplete support
Projects with custom allocators that cannot be swapped out
Short-lived programs where memory fragmentation is negligible

Pros & Cons

Pros

Transparent Fragmentation Recovery

Automatically compacts memory to reduce fragmentation without any code changes, as shown in the Redis example where it meshed over 100 MB.

Drop-in Integration

Serves as a direct malloc replacement via LD_PRELOAD or linking, allowing seamless use with existing C/C++ applications like git.

Minimal Runtime Dependencies

Requires only libc at runtime with no additional dependencies, making deployment lightweight and straightforward.

Built-in Performance Monitoring

Provides statistics via the MALLOCSTATS environment variable to track compaction effectiveness, such as meshed MB and allocation counts.

Cons

Incomplete Cross-Platform Support

Windows support is still a work in progress, limiting Mesh to Linux and macOS environments only.

Variable Effectiveness

Not all workloads experience fragmentation, so Mesh may have minimal impact on some applications, as admitted in the README.

Build System Complexity

Relies on Bazel wrapped in a Makefile, which can add setup overhead compared to simpler build systems like CMake or plain Make.

Frequently Asked Questions

Home

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#systems-programming

#malloc-replacement

#memory-management

#performance-optimization

#fragmentation-reduction