Dynamic Memory Alignment (DMA)

• Dynamic Memory Alignment (DMA) is a set of techniques that ensure memory is allocated in hardware-specified aligned regions at runtime for optimal performance.
• It leverages adaptive allocation logic and metadata-tracking to match diverse hardware constraints, improving data transfer and reducing latency.
• DMA methods coalesce transfers and support pipelining strategies, leading to significant gains in throughput and resource utilization across accelerator and in-memory systems.

Dynamic Memory Alignment (DMA) encompasses a set of methodologies and practical strategies for ensuring that memory objects are allocated in dynamically sized, but strictly specified, aligned regions at runtime. Across accelerator software, low-precision GPU compute, and memory-centric storage/processing, the term describes host-side or device mechanisms by which memory is aligned to hardware- or algorithm-specific constraints—such as cache lines, bus burst widths, DRAM rows, or device-local pitch. This alignment is not fixed at compile time but must be adaptively maintained as object sizes and access patterns evolve during application execution. The breadth of DMA’s impact ranges from raising compute resource utilization and eliminating padding inefficiencies (as in TMA/FP8 workloads), to enabling in-place memory operations in PuD systems, and even to maintaining quality of information retrieval in retrieval-augmented generation (RAG) by adapting working memory under feedback-driven alignment protocols.

1. Definitions and Scope of Dynamic Memory Alignment

Dynamic Memory Alignment refers to the process of enforcing runtime-appropriate address and size constraints on memory regions based on hardware DMA engine, accelerator, GPU, or memory substrate requirements. The alignment can be at coarse granularity (e.g., 2 MB hugepages for DRAM row locality in PuD (Oliveira et al., 7 Mar 2024)) or fine granularity (e.g., 64–256 B for AXI bus burst boundaries (Haris et al., 29 Feb 2024), 16 B global/128 B shared for Hopper TMA (Su et al., 7 Aug 2025)). The distinguishing factors of DMA as opposed to static alignment are:

• Allocation and address alignment must be determined according to actual size and placement at runtime.
• Alignment requirements may depend on multidimensional object shapes, as for tensor buffers.
• The allocation logic must keep track of both raw and aligned pointers, and the exact stride or pitch implied by the lower-level transport.
• Alignment is essential not just for correctness (e.g., legal bus operations) but for performance, as improper alignment can result in unaligned bursts, extra latency, or fallback to slow, software-based routines.

Applications include host-accelerator data movement (Haris et al., 29 Feb 2024), grouped GEMM kernels under variable grouping (Su et al., 7 Aug 2025), and DRAM in-memory processing primitives (Oliveira et al., 7 Mar 2024).

2. Patterns and Algorithms for Dynamic Alignment

AXI4MLIR DMA-Based Buffer Allocation

In host-to-accelerator scenarios, such as AXI4MLIR-based flows (Haris et al., 29 Feb 2024), every transfer region assigned for DMA must align to at least the DMA engine’s burst size. This is achieved by:

• Rounding allocations to multiples of alignment size (N), mathematically:

$$A' = \Bigl\lceil \frac{A}{N}\Bigr\rceil \times N \quad,\quad \text{size}' = \Bigl\lceil \frac{\text{requested\_size}}{N} \Bigr\rceil \times N$$

• Using RAII C++ wrappers around posix_memalign() to create guaranteed-aligned, physically contiguous buffers.
• Passing alignment metadata through the compiler toolchain (MLIR lowering), for example:

  %A_dma = memref.alloc() : memref<64 x f32>, alignment = 64, #dma

TMA-Adaptive Grouped GEMM Descriptor Sets

On Hopper GPUs, as seen in TMA-Adaptive FP8 Grouped GEMM (Su et al., 7 Aug 2025), dynamic alignment manages both memory addresses and transfer descriptors:

• Maintaining a logarithmic pool of TMA descriptors:

$$\mathcal{D}_{\text{pool}} = \left\{[2^i,\, \mathtt{block\_N}]\,|\, 0 \leq i \leq \lfloor\log_2(\mathtt{block\_M})\rfloor\right\}$$

to cover all residual rows without padding.

• At runtime, selecting the minimal cover for the group dimension:

$$r^g = M^g \bmod \mathtt{block\_M}, \quad d = 2^{\lfloor \log_2(r^g)\rfloor}$$

and issuing two aligned TMA store operations, each respecting device alignment (16 B global, 128 B shared).

PUMA OS-Level Region Tracking

In DRAM-processing platforms (Oliveira et al., 7 Mar 2024), dynamic alignment requires OS/driver-level interventions:

• Allocating memory at sub-page granularity such that each region exactly matches a DRAM row’s physical boundaries.
• Maintaining subarray locality by mapping allocation requests to physical regions within the same subarray, using platform DRAM mapping functions.
• Presenting logically contiguous, but physically noncontiguous, aligned buffers to user space via per-region mmap and kernel-virtual mapping.

3. Coalescing and Pipelining: Optimizing DMA-Aware Transfers

Proper alignment enables higher-level optimizations that further exploit hardware capabilities:

• Transfer Coalescing (AXI4MLIR): Adjacent, aligned buffers can be concatenated into a larger single DMA transaction, reducing round-trips and arbitration overhead. The decision algorithm checks that:
  1. Physical addresses are contiguous,
  2. Both segments satisfy alignment, and
  3. Combined size does not exceed the engine’s burst limit.

For example, in the MatMul case paper, coalescing reduced start/stop cycles by ≈40% and improved bandwidth from ∼2.1 GB/s to ∼2.9 GB/s (Haris et al., 29 Feb 2024).

• Software Pipelining/Double Buffering:

By overlapping buffer load (DMA from host), accelerator compute, and store (DMA to host), pipeline latency is reduced. The steady-state throughput,

$$T_{\text{steady}} = \max\{T_{\text{load}} + T_{\text{compute}}, T_{\text{store}}\}$$

demonstrates a 22% reduction in end-to-end latency for the MatMul tile benchmark.

• Two-Phase Load-Store (TMA GEMM):

Dynamic selection and overlap-safe stores enable partial groups to be transferred efficiently without erroneous or out-of-bounds writes. The overlap of up to $2d - r^g$ rows is safe because those entries contain identical data.

These algorithms critically depend on buffer and pitch alignment as a precondition for their legality and hardware efficiency.

4. Device- and Substrate-Specific Alignment Requirements

Alignment constraints are highly substrate- and architecture-specific, necessitating tailored DMA methodologies:

Platform/Engine	Alignment Constraint	Enforcement Mechanism
AXI DMA (FPGA/SoC)	Bus burst (e.g., 64–256 B)	posix_memalign, MLIR attr, driver check
NVIDIA Hopper TMA	16 B global, 128 B shared mem	Descriptor pool, pitch rounding, overfetch
DRAM PUD (RowClone/Ambit)	Row boundary, subarray	OS kernel region allocator, hugepage split

For example, Hopper requires that data loaded or stored via TMA for grouped GEMM lands on both a 16-byte aligned global address and 128-byte shared-memory aligned pitch, enforced via block size and overfetch (Su et al., 7 Aug 2025).

In PuD systems, DRAM row alignment (column offset zero) and same-subarray constraint are enforced by custom allocation routines that subdivide hugepages, with each sub-region tracked and remapped as required (Oliveira et al., 7 Mar 2024).

5. Empirical Impact and Performance Outcomes

DMA techniques yield measurable system-level and application-level improvements. In MatMul accelerator experiments using AXI4MLIR (Haris et al., 29 Feb 2024):

• Baseline accelerator utilization was below 10% due to unaligned, non-coalesced DMA copies.
• DMA-based allocation removed ≈15% of CPU blocking time.
• Coalesced transfers improved effective memory throughput by ≈30% (utilization up to ~35%).
• With full pipelining and alignment, accelerator busy-time exceeded 85%, and end-to-end speedup was 3.2× over unoptimized flows.

In TMA-Adaptive FP8 grouped GEMM tasks (Su et al., 7 Aug 2025):

• Padding-free dynamic alignment yielded 1.7–20.4% speedup and up to 23.8% memory reduction compared to state-of-the-art padding-based approaches, with perfect numerical equivalence on valid (unpadded) data.
• Most pronounced gains arose for workloads with many, small groups.

For PUD architectures (Oliveira et al., 7 Mar 2024):

• Naïve allocators (e.g., malloc) yielded 0% in-DRAM execution for operations like RowClone/Ambit, as misaligned addresses forced fallback to CPU-based slow paths.
• PUMA allocator raised this figure up to 100%, with 3–5× performance over standard allocators on large buffers due to alignment-enforced DRAM internal operations.

6. Design Trade-offs, Limitations, and Broad Applicability

Deployment of dynamic memory alignment strategies involves several trade-offs:

• Fragmentation: Fine-grained alignment (especially by subarray/row) fragments physical memory. “Worst-fit” region-picking in kernel pools mitigates this but cannot eliminate it entirely.
• Resource reservation: Substrate-specific alignment (e.g., huge page pools for DRAM region mapping) can conflict with other system demands.
• Software complexity: Integrating dynamic alignment schemes (kernel modules, address mapping, compiler passes) increases software stack complexity, but modularization (as in PUMA) can isolate this burden.

DMA concepts generalize to any context where high throughput, legal hardware transactions, or specialized in-place data movement require strict, yet runtime-driven, address and stride alignment. Examples include certain GPU DMA engines with bank interleaving and in-die compute/storage integration (Oliveira et al., 7 Mar 2024).

7. Summary and Cross-Domain Connections

Dynamic Memory Alignment is foundational to maximizing entire-system efficiency in accelerator coupling (host ↔ device DMA), low-precision neural compute (TMA-aligned GEMM), and in-memory compute primitives (RowClone/Ambit). While the implementation specifics and tuning parameters differ by hardware substrate, all effective DMA strategies share core traits:

• Runtime-aware logic for alignment and allocation
• Metadata-tracking for correct free/reclaim
• Coordination with higher-level optimization (coalescing, pipelining)
• Device-local constraints baked into both compiler and runtime

The empirical results demonstrate that such approaches can turn sub-utilized, bottlenecked hardware into high-throughput, near-peak efficiency resources across a diversity of domains (Haris et al., 29 Feb 2024, Su et al., 7 Aug 2025, Oliveira et al., 7 Mar 2024). A plausible implication is that further integration of compiler, OS, and hardware feedback mechanisms will be essential to address evolving device landscapes and workload dynamism.