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Memory Allocation Problems Limit Memory Allocation (if not necessary) Multithreaded programs often do not scale because the heap is a bottleneck. When multiple threads simultaneously allocate or deallocate memory from the allocator, the allocator will serialize them. Programs making intensive use of the allocator actually slow down as the number of processors increases. Malloc (libc) is the worst memory allocation API to use. Programs should avoid, if possible, allocating/deallocating memory too often and in particular whenever a packet is received. In the Linux kernel there are available kernel/driver patches for recycling skbuff (kernel memory used to store incoming/outgoing packets). Using PF_RING (into the driver) for copying packets from the NIC to the circular buffer without any memory allocation increases the capture performance (around 10%) and reduces congestion issues. Design Evolution Basic design of malloc() is to dynamically pre-allocate a pool of memory from the OS in which applications can then take smaller pieces from. malloc() is a standard API having a choice of different allocation algorithms and to mitigate the expensive OS system calls (typically done at program initialization time) during allocation of its system memory. The first memory allocation scheme started with a stack-based memory allocation. Next came the dynamic-based memory allocation scheme where linked-list and bucket-heap mechanism are used to divide the private-heap using size class approach. Soon, garbage collection algorithm introduced the initial backend of the memory allocation scheme. Frontend covers the usual malloc() API, et al. In 2006, a third pool was introduced (after operating system memory pool and library-based memory pool) called the “arena”. Arena is a jemalloc-term and is intended to deal with different memory types such as different-speed memory bank or NUMA-architecture, as well as memory tied to specific to each of the multiple CPU core or even CPU infinity. Frontend Evolution Frontend manages the memory being given to the application. Within the frontend of the memory allocation system, the evolution went in the following order: link-list free space heap-bucket size classes (eliminating an object header) (Process) Owner encoding single core local allocation buffers (CLABs) Epoch encoding Large-size class memory block by direct mmap() Hazard pointers (safe memory reclamation for lock-free objects) (M.M. Michael, 2004) Arena memory pool (CPU/core and thread, separately) thread-specific local allocation buffers (TLABs) constant-time modulo synchronization (early return to OS pool, or FreeBSD madvise call) Backend Evolution Backend of the memory allocation system manages the empty, straggling, fragmented or no-longer used memory blocks back to the OS (thereby reducing RSS). Pool semantic: Remote f-list encoding, using Treiber stack), (R.K. Treiber, 1986) buddy algorithm binary buddy algorithm BIPOP Table (span-based allocator)(S. Schneider, 2006) aka local free list and remote free list segment queue (Quasi-linearizability, Y. Afek, 2010) multi-core distributed queue (A. Haas, 2013) k-FIFO queue (T.A.
Henzinger, 2013) Competition There are better ones out there that does not worsen as more threads/processes performs memory allocation system calls; they are listed in best-to-good performance order [seed with source]: Comparison of malloc design Allocator Origin / Maintainer Thread Safe Per-Thread Cache Multi-Arena / Heaps Lock-Free Fast Path NUMA Aware Fragmentation Control Notes dlmalloc Doug Lea No No No No Low Single global heap; basis for many later allocators ptmalloc2 / ptmalloc3 glibc Yes Limited Yes No Medium glibc default; arena locks cause contention glibc malloc (current) GNU Yes Limited Yes No Medium Wrapper around ptmalloc with tunables jemalloc FreeBSD / Meta Yes Yes Yes Partial High Thread-arena affinity reduces CAS contention tcmalloc Google Yes Yes Yes Partial Medium-High Per-CPU caches; central freelists still exist mimalloc Microsoft Yes Yes Yes Yes High Designed to minimize atomic ops and false sharing Hoard Emery Berger Yes Yes Yes Partial Medium Focus on scalability and false-sharing avoidance nedmalloc NEDMALLOC Yes Yes Yes No Medium dlmalloc-derived with thread caching phkmalloc FreeBSD Yes Yes Yes No Medium Early FreeBSD allocator family libumem Solaris Yes Yes Yes Yes Medium-High Solaris allocator with debugging and locality support mtmalloc Solaris Yes Yes Yes Yes Medium Solaris multithreaded allocator snmalloc Microsoft Research Yes Yes Yes Yes High NUMA-aware security- and scalability-focused lockless malloc (research) Academic / Experimental Varies Yes Varies Yes Low Often CAS-heavy; not production ready ltalloc Academic CAS, Atomic Contention Characteristics CAS / Atomic Contention characteristics Allocator Estimated Atomics per alloc/free Shared Cacheline Risk CAS Contention Sensitivity Notes dlmalloc High High Very High Global structures and locks dominate ptmalloc2 / ptmalloc3 Medium-High High High Arena locks cause cacheline bouncing glibc malloc (current) Medium-High High High Wrapper around ptmalloc jemalloc Low Low Low Arena-local metadata; minimal shared CAS tcmalloc Low-Medium Medium Medium Per-CPU caches; central freelist CAS mimalloc Very Low Very Low Very Low Designed to minimize atomic ops Hoard Medium Medium Medium Reduces false sharing but still synchronized nedmalloc Medium Medium Medium Thread caches reduce but don’t eliminate CAS phkmalloc Medium Medium Medium Older FreeBSD design libumem Low Low Low Lock-free fast paths on Solaris mtmalloc Low Low Low Per-thread structures reduce atomic sharing snmalloc Very Low Very Low Very Low Message-passing model avoids shared CAS lockless malloc (research) High High High Often CAS-heavy despite no locks NUMA, Memory Locality characteristics NUMA / Memory Locality characteristics Allocator Explicit NUMA Support First-Touch Friendly Cross-NUMA Traffic Risk Locality Preservation Notes dlmalloc No Yes Very High Poor Single heap across nodes ptmalloc2 / ptmalloc3 No Partial High Fair Arenas not NUMA-bound glibc malloc (current) No Partial High Fair Relies on OS placement jemalloc Partial Yes Medium Good Optional NUMA arena tuning tcmalloc Limited Yes Medium Fair CPU caches not NUMA-aware mimalloc No Yes Low Very Good Strong thread locality Hoard No Yes Medium Good Per-processor heaps help nedmalloc No Yes Medium Fair Thread caches but global fallback phkmalloc Partial Yes Medium Fair Early locality optimizations libumem Yes Yes Low Very Good Solaris NUMA policies mtmalloc Yes Yes Low Very Good Designed for NUMA Solaris systems snmalloc Yes Yes Very Low Excellent NUMA-first architecture lockless malloc (research) No Varies High Poor Locality rarely addressed Benchmark-Oriented Practical Performance Benchmark-Oriented Practical Performance Allocator Small Alloc Throughput Large Alloc Throughput Latency Under Contention Memory Overhead Fragmentation Risk Notes dlmalloc Low Medium Poor Low High Not suitable for multithreaded loads ptmalloc2 / ptmalloc3 Medium Medium Poor Medium Medium glibc default glibc malloc (current) Medium Medium Poor Medium Medium Tunable but limited jemalloc High High Very Good Medium-Low Low Excellent all-around allocator tcmalloc Very High Medium Good Medium Medium Optimized for small objects mimalloc High High Excellent Low Low Great latency predictability Hoard Medium Medium Good Medium Low Designed for scalability nedmalloc Medium Medium Fair Medium Medium Older but usable phkmalloc Medium Medium Fair Medium Medium Historical FreeBSD allocator libumem High Medium Very Good Medium Low Strong debugging support mtmalloc High Medium Very Good Medium Low Enterprise Solaris workloads snmalloc High High Excellent Low Very Low Security + scalability focus lockless malloc (research) Varies Varies Poor Low High Often unstable in practice Allocator Recommendation Allocator Recommendation Workload Type Primary Bottleneck Key Risks Recommended Allocator Why It Fits Alternatives Avoid Highly Contended Multithreaded Atomic/CAS latency Cacheline bouncing jemalloc Multi-arena + thread affinity minimizes shared CAS mimalloc snmalloc,dlmalloc ptmalloc Low-Latency / Tail-Sensitive Allocation jitter Lock convoying mimalloc Very low atomic count and predictable fast paths snmalloc jemalloc,tcmalloc NUMA / Multi-Socket Servers Cross-node memory access Remote cache ownership snmalloc Explicit NUMA awareness and locality control jemalloc (NUMA tuned) libumem,glibc malloc Small Object Heavy (RPC / Web) Allocator throughput Central freelist contention tcmalloc Per-CPU caches optimized for small allocs jemalloc mimalloc,ptmalloc Large Object / Mixed Sizes Fragmentation TLB pressure jemalloc Excellent fragmentation control and extent management mimalloc glibc malloc False-Sharing Sensitive Cacheline ping-pong Metadata sharing Hoard Designed to avoid false sharing jemalloc mimalloc,dlmalloc Security-Hardened Use-after-free exploits Heap corruption snmalloc Isolation + security invariants mimalloc (secure) ptmalloc Debugging / Leak Detection Memory misuse visibility Silent corruption libumem Strong runtime diagnostics jemalloc (profiling) tcmalloc Embedded / Low Memory Footprint size Overhead dlmalloc Small and simple if single-threaded nedmalloc jemalloc Real-Time / Deterministic Unbounded latency OS interference mimalloc Low variance fast paths snmalloc jemalloc,tcmalloc HPC / Scientific NUMA Memory bandwidth Remote NUMA hits snmalloc NUMA-first design and low CAS traffic jemalloc + mbind glibc malloc Legacy / Compatibility ABI stability Toolchain issues glibc malloc System default and safest fallback ptmalloc Decision Chart for Malloc Selection Graphviz DOT file [http://www.phrack.org/issues.html?issue=57&id=8#article] [https://sploitfun.wordpress.com/2015/03/04/heap-overflow-using-malloc-maleficarum/] [http://phrack.org/issues/66/10.html] References R.