Performance optimizations: TLAB fast path, realloc, sharded global heap, and CPU-based heap selection

[emeryberger]

Summary

This PR introduces several performance optimizations to Hoard:

1. TLAB Fast Path Optimizations

• Skip magic number validation on free fast path (trust freelist pointers)
• Skip normalize() on fast path (require exact pointer per C standard)
• Add getObjectSizeUnchecked() to avoid redundant validation
• Use per-bin object counts instead of byte arithmetic for threshold checks

2. Custom xxrealloc Implementation

• Avoid double malloc_usable_size lookup (was calling it twice per realloc)
• Significant speedup for realloc-heavy workloads

3. Sharded Global Heap

• Reduces contention on the global heap with configurable shards (default 8)
• NUMA-aware via sched_getcpu() on Linux for locality
• Preserves blowup bounds through careful design (see below)

4. CPU-Based Heap Selection

• Replace thread-ID-based heap selection with CPU-based selection on Linux
• Dramatically improves performance at high thread counts (3-4x faster)
• Preserves memory efficiency

Benchmark Summary

All graphs normalized to Hoard (1.0 = Hoard, shown as green line). Values above the line mean worse than Hoard.

Execution Time

Memory Usage

Why Thread-ID-Based Heap Selection Was Problematic

The old approach used tid % NumHeaps to assign threads to heaps. This had two fundamental problems:

1. Thread IDs don't correlate with concurrent execution. When a thread exits, its ID slot becomes unused, but the ID isn't recycled immediately. New threads get fresh IDs that may hash to heaps already used by active threads, causing contention. Meanwhile, heaps assigned to exited threads sit idle.

2. No NUMA awareness. Threads on different NUMA nodes could hash to the same heap, causing cross-node memory traffic. On our 192-core test system (2 NUMA nodes), this caused up to 1.8x slowdown due to remote memory access.

The new approach uses sched_getcpu() % NumHeaps:

• Threads on the same CPU use the same heap (cache locality)
• CPUs on the same NUMA node tend to use nearby heap indices (NUMA locality)
• Load naturally distributes across actually-executing threads, not historical thread IDs

Sharded Global Heap: Locality and Blowup Bound

The sharded global heap uses different strategies for put() and get() to achieve both NUMA locality and memory efficiency:

put() — Local shard for NUMA locality

put(): shard = sched_getcpu() & (NumShards - 1)

Superblocks are returned to the CPU-local shard. This preserves NUMA locality because:

• Physical pages were allocated near the thread that last used them (first-touch policy)
• Returning to a local shard keeps the superblock near its physical memory
• Threads on the same NUMA node share shards, increasing reuse probability

get() — Power-of-two choices from fuller shard

get(): try local shard first
       then pick two random shards, take from the FULLER one
       fallback: try all shards sequentially

This design maintains the blowup bound because:

1. No memory stranding: The fallback ensures any superblock in any shard can be found. A thread is never forced to allocate fresh memory when a reusable superblock exists somewhere.

2. Fuller-first concentrates superblocks: Taking from the fuller shard (power-of-two choices) causes superblocks to concentrate in fewer shards over time. This improves reuse and reduces fragmentation.

3. Total capacity unchanged: Sharding is purely internal reorganization. The sum of all shards equals what the single global heap held before.

4. Emptiness threshold unchanged: Per-thread heaps still return superblocks at the same threshold. Sharding doesn't change when superblocks move, only where they're stored.

Why the Blowup Bound Matters

Hoard guarantees memory consumption is bounded by O(U + c·P·S·log M) where:

• U = memory currently in use by the application
• c = a small constant
• P = number of processors (threads)
• S = superblock size (256KB)
• M = maximum memory ever allocated by the application

This bound matters for several reasons:

1. Predictable overhead: Memory consumption scales with actual usage (U) plus a logarithmic term. An application using 1GB will not suddenly consume 10GB due to fragmentation.

2. Scalability: The P·S·log(M) term means per-thread overhead grows only logarithmically with allocation history, not linearly. A long-running server won't accumulate unbounded fragmentation.

3. No pathological cases: Some allocators can exhibit O(M) blowup under adversarial or unlucky allocation patterns—memory proportional to peak usage even after most is freed. Hoard's bound prevents this.

4. Production safety: For memory-constrained environments (containers, embedded systems), a formal bound lets you provision memory with confidence.

The global heap memory is counted as "available to all threads" in this analysis. Since any superblock remains findable via the fallback (no stranding), the sharded design preserves the bound.

Design Notes: Comparison with Other Allocators

We benchmarked against mimalloc, jemalloc, and glibc. While all three specialized allocators target scalable multithreaded allocation, the approaches differ significantly:

What mimalloc does differently

• Segment-page architecture: mimalloc uses 64MB segments containing multiple 64KB pages of different size classes, allowing better cache locality and reduced metadata overhead
• No blowup guarantee: mimalloc optimizes for speed without Hoard's O(U + c·P·S·log M) memory bound
• Thread-local pages: mimalloc keeps pages thread-local with deferred free

What jemalloc does differently

• Extent-based allocation: jemalloc uses extents (variable-sized runs of pages) managed by a radix tree, with thread caches (tcaches) for small objects
• Explicit dirty page decay: jemalloc uses time-based decay to return dirty pages to the OS, providing good memory efficiency on long-running processes
• Arena sharding: jemalloc partitions allocations across multiple arenas (typically 4x CPU count), with threads assigned to arenas round-robin
• No formal blowup bound: Like mimalloc, jemalloc optimizes empirically rather than providing theoretical guarantees

What Hoard does (unchanged fundamentals)

• Superblock architecture: 256KB aligned superblocks, one size class per superblock
• Emptiness classes: Superblocks categorized by fullness for efficient memory reclamation
• Blowup bound: Guaranteed memory overhead O(U + c·P·S·log M)

New optimizations (this PR)

• Sharded global heap: Inspired by concurrent data structure research (power-of-two choices from Azar et al.). Reduces global heap contention while preserving Hoard's superblock redistribution semantics. Similar in spirit to jemalloc's arena sharding, but applied to the global heap rather than per-thread allocation.
• CPU-based heap selection: Uses sched_getcpu() instead of thread ID hashing for NUMA-aware heap assignment.
• TLAB fast path: Removes redundant validation on the hot path. All modern allocators minimize fast-path overhead; this brings Hoard in line.
• Custom realloc: Avoids redundant size lookups. Standard optimization.

The key distinction: these optimizations make Hoard faster while preserving its memory efficiency guarantees, rather than trading memory bounds for speed.

Detailed Benchmark Results (192-core, 2-node NUMA system)

All benchmarks run with LD_PRELOAD to inject each allocator.

Larson (server workload simulation)

./larson 5 8 1000 5000 100 4141 <threads>

Threads	Hoard	mimalloc	jemalloc	glibc
16	69MB	128MB	93MB	115MB
32	110MB	234MB	185MB	230MB
64	222MB	517MB	424MB	444MB
128	714MB	1167MB	928MB	882MB
192	929MB	1918MB	1639MB	1291MB
256	1344MB	2576MB	2364MB	1687MB

threadtest (per-thread malloc/free throughput)

./threadtest <threads> 100000 50000 0 8

Threads	Hoard	mimalloc	jemalloc	glibc
16	2.89s / 8MB	3.07s / 9MB	7.90s / 9MB	6.49s / 8MB
32	1.52s / 8MB	1.55s / 10MB	3.83s / 14MB	3.42s / 9MB
64	2.41s / 9MB	0.74s / 11MB	1.94s / 17MB	1.66s / 9MB
128	0.94s / 10MB	0.43s / 14MB	1.27s / 22MB	0.98s / 10MB
192	0.48s / 10MB	0.35s / 16MB	0.94s / 28MB	0.76s / 11MB
256	0.42s / 11MB	0.43s / 18MB	1.01s / 28MB	0.90s / 11MB

linux-scalability (malloc/free pairs)

./linux-scalability <threads> 1000000 8

Threads	Hoard	mimalloc	jemalloc	glibc
16	0.066s / 183MB	0.046s / 182MB	0.044s / 62MB	0.160s / 229MB
32	0.095s / 303MB	0.067s / 302MB	0.047s / 62MB	0.216s / 351MB
64	0.142s / 543MB	0.116s / 542MB	0.064s / 74MB	0.422s / 644MB
128	0.226s / 1035MB	0.211s / 1034MB	0.091s / 86MB	0.590s / 1154MB
192	0.449s / 2018MB	0.369s / 1526MB	0.073s / 74MB	1.003s / 1616MB
256	0.248s / 1796MB	0.177s / 2028MB	0.104s / 129MB	1.562s / 2115MB

Phong (realloc-heavy workload)

./phong -t<threads> -a<allocations>
# allocations: 100000 (4-8t), 500000 (16-256t)

Threads	Hoard	mimalloc	jemalloc	glibc
4	2.08s / 203MB	4.42s / 101MB	6.82s / 100MB	10.19s / 109MB
8	0.43s / 204MB	0.97s / 107MB	1.37s / 101MB	2.14s / 110MB
16	4.45s / 995MB	9.04s / 488MB	13.02s / 481MB	18.41s / 529MB
32	1.28s / 1000MB	1.83s / 510MB	2.77s / 487MB	4.14s / 541MB
64	0.43s / 1012MB	0.49s / 560MB	0.64s / 508MB	1.11s / 565MB
128	0.37s / 1014MB	0.23s / 640MB	0.25s / 520MB	0.61s / 550MB
192	0.40s / 1054MB	0.19s / 622MB	0.22s / 535MB	0.56s / 529MB
256	0.45s / 1043MB	0.19s / 614MB	0.17s / 510MB	0.54s / 534MB

Test plan

• Larson benchmark at 16-256 threads
• threadtest benchmark at 16-256 threads
• linux-scalability benchmark at 16-256 threads
• Phong benchmark (realloc-heavy) at 4-256 threads
• NUMA microbenchmark confirms 1.8x cross-node penalty
• Verified no memory blowup on Larson (Hoard uses less memory than mimalloc and jemalloc)
• Compared against mimalloc, jemalloc, and glibc

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