GPU Allocator: Design & Performance

• GPU Allocator is a system component that manages GPU compute and memory resources using distributed, local, and dynamic strategies to enhance utilization and efficiency.
• Designs range from local device-level resource sharing to distributed reinforcement learning-based schedulers that balance workloads across regions and time slots.
• Allocation strategies address memory fragmentation, dynamic object allocation, and real-time constraints, enabling higher throughput and reliable deep learning and inference.

A GPU allocator is a system component or software module responsible for managing allocation and deallocation of GPU computing and memory resources to concurrent tasks or jobs, often under highly variable and resource-constrained workloads. In modern multi-GPU systems—such as distributed inference clusters for LLMs, deep learning training frameworks, and real-time embedded deployments—allocation schemes have direct impact on utilization, throughput, latency, and power efficiency. The design space includes local device-level allocators (e.g., per-SM resource partitioners, dynamic memory managers), distributed and cluster-aware spatio-temporal schedulers, and specialized approaches targeting predictability or defragmentation.

1. Distributed and Spatio-Temporal Allocation Frameworks

The scaling of LLM inference clusters and geographically distributed serving platforms has motivated GPU allocators to operate as distributed, temporally aware systems. "Cross-Timeslot Optimization for Distributed GPU Inference Using Reinforcement Learning" (TORTA) decomposes the allocator into two key layers (Du et al., 14 Jul 2025):

• Macro-level inter-region scheduler: Operates at a coarse timeslot (5–15 min) granularity. It forecasts future regional request loads and computes an optimal transport (OT) plan for spatio-temporal load balancing, subsequently refined via reinforcement learning (RL) to generate a routing matrix $A_t$ that adapts smoothly across time, minimizes network cost, operational cost, and penalizes abrupt migration ("switching cost").
• Micro-level intra-region allocator: Within each region, this system activates servers based on anticipated queue and workload, amortizing warm-up overheads, and greedily matches tasks to servers according to a multidimensional compatibility score (hardware fit, queue/utility, data locality).

This two-layer separation—coarse RL+OT global allocation and local heuristic scheduling—improves average inference response by up to 15%, reduces operational cost by 10–20%, and balances the network under diverse LLM serving topologies, while explicitly penalizing volatile reconfiguration (Du et al., 14 Jul 2025).

2. GPU Device-level Resource Allocators

For single or multi-GPU configurations, device-level allocators manage physical compute (thread-blocks, warps), register files, scratchpad (shared) memory, and local VRAM. Conventional thread-block allocators suffer from wasted resources due to non-exact fit of block resource requirements (registers, shared memory) within Streaming Multiprocessors (SMs). "Improving GPU Performance Through Resource Sharing" introduced:

• Register and scratchpad sharing: Dynamic sharing between pairs of thread blocks allows $\sim90\%$ utilization of SM resources, increasing occupancy and hiding execution latency. Hardware support (per-block or per-warp lock bits, local and global bitmaps) enables safe, deadlock-free sharing, coordinated with barrier (e.g., __syncthreads()) synchronization.
• Scheduling enhancements: Owner-Warp-First (OWF) prioritizes warps holding shared resources to minimize blocking, while dynamic warp throttling controls cache pollution from non-owner warps.

Benchmarking on GPGPU-Sim demonstrated up to 30% throughput gains in resource-constrained workloads (Jatala et al., 2015).

3. Memory Fragmentation and Allocation Strategies for Deep Learning

Applications such as large-scale DNN training introduce demands for massive, highly irregular memory allocations, exacerbated by optimizations (recompute, offloading, pipeline parallelism) that disrupt tensor lifespan regularity.

• Online Caching Allocators: PyTorch and TensorFlow employ best-fit caching allocators with splitting/coalescing. Under heavy, non-uniform deallocation, internal fragmentation can reach 43%, leading to inefficient memory usage, OOM events, and restricted batch or model sizes (Huang et al., 22 Jul 2025).
• Spatio-Temporal Planning Allocators: STWeaver combines offline profiling and grouping methods (homophase, homosize clustering) to synthesize near-optimal static allocation plans, augmented online by dynamic allocation for unpredictable requests (e.g., MoE). This results in $\sim80\%$ lower fragmentation and enables larger, higher-throughput model deployments with negligible runtime penalty (Huang et al., 22 Jul 2025).
• Virtual Memory Stitching: GMLake leverages low-level CUDA VMM APIs (cuMemAddressReserve, cuMemMap, etc.) to "stitch" noncontiguous physical allocations into virtually contiguous ranges, enabling substantial defragmentation (up to 25 GB, $\sim$15% lower fragmentation) and high utilization across distributed and memory-reduced training settings (Guo et al., 16 Jan 2024).

4. Dynamic Allocators for Concurrent Parallel Data Structures

Several dynamic GPU allocators are tailored for fine-grained, high-throughput allocation patterns within parallel data structures:

• SlabAlloc: Implements a warp-synchronous allocation protocol using resident block assignment and per-warp register-local bitmaps for managing fixed-size allocation units ("slabs"). This design enables line-rate throughput of up to 600 million allocations per second, 37$\times$ faster than prior GPU dynamic allocators (Ashkiani et al., 2017).
• DynaSOAr: A lock-free, heap-allocated object allocator for SMMO (single-method, multiple-objects) workloads. Uses block-local SOA layout, hierarchical two-level bitmaps for allocation metadata, and coalesced group allocation to reduce both fragmentation and atomic contention. Real-world benchmarks show up to 3$\times$ speedup and 2$\times$ larger problem sizes compared to hash-based allocators (Springer et al., 2018).
• GGArray: Constructs dynamically growable arrays completely on the GPU using block-local LFVector segments with doubling-bucket allocation and O(1) atomic add for insertions. It achieves competitive insertion and resize throughput (within $\sim1.5\times$ of semi-static arrays), but incurs lower regular R/W bandwidth due to pointer-chasing (Meneses et al., 2022).

5. Address Translation and Memory Management Innovations

Page-level allocation and address translation are critical for supporting large working sets while maintaining low translation latency and efficient paging.

• Mosaic: Provides application-transparent multi-page-size support by aligning bulk base-page (4 KiB) allocations contiguously in physical memory, enabling subsequent hardware coalescing into large pages (2 MiB) without data migration. Splintering and intra-GPU compaction reclaim fragmented pages, achieving close-to-ideal TLB reach, reducing TLB miss rates ($\sim$70–80% lower latency), and maintaining demand paging at base-page granularity (Ausavarungnirun et al., 2018).

6. Predictability, Scheduling, and Real-time Considerations

For real-time or embedded GPU workloads requiring temporal predictability, server-based GPU allocator architectures centralize device access:

• Server-based Predictable Allocation: A dedicated high-priority server task serializes GPU requests from clients (user tasks), guaranteeing bounded response time by suspending client tasks during GPU use and providing analytic worst-case latency bounds via fixed-point analysis. This approach eliminates CPU busy-waiting and reduces priority inversion compared to synchronization-based models, improving real-time schedulability and system responsiveness (Kim et al., 2017).

7. Analytical Approaches to Peak Memory Estimation

For job-level scheduling and shared-cluster resource planning, estimation of peak memory requirements is critical:

• xMem: Provides a CPU-only dynamic analysis pipeline, reconstructing a high-fidelity simulation of CUDA caching-allocator behavior from initial CPU profile traces of the target application. By emulating allocator nuances (segmentation, alignment, caching), xMem reduces median relative error by 91% compared to static/dataset-driven predictors, drops OOM probability from 30% to under 7%, and enables 3.68$\times$ increased memory conservation in multi-job packing scenarios (Shi et al., 23 Oct 2025).

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In sum, the GPU allocator research landscape encompasses distributed RL- and OT-modulated spatio-temporal scheduling (Du et al., 14 Jul 2025), fine-grained device-level resource sharing (Jatala et al., 2015), fragmentation-aware memory management (Huang et al., 22 Jul 2025, Guo et al., 16 Jan 2024), dynamic and lock-free in-GPU allocation (Ashkiani et al., 2017, Springer et al., 2018, Meneses et al., 2022), translation-aware allocators (Ausavarungnirun et al., 2018), and real-time guarantee-driven schedulers (Kim et al., 2017), converging toward higher utilization, predictability, and scalability under increasingly heterogeneous, large-scale deployment scenarios.