Systems and methods for adaptive optimization and coordination of data layout and execution on parallel processing architectures

Abstract

Systems and methods for optimizing runtime efficiency on parallel processors are disclosed. Workloads are transformed into SoA, AoSoA, or matrix forms aligned to GPU / accelerator architecture. Runtime monitoring triggers adaptive restructuring to minimize divergence and maximize memory coalescing. Workloads are dispatched across heterogeneous processors and cloud clusters. In certain embodiments, game states are represented as structured matrices and synchronized via vector deltas between clients and servers. In further embodiments, financial order books are processed as price-aligned sub-matrices with microbatch commits under price-time priority, enabling deterministic, low-latency matching and risk evaluation. The approach is domain-agnostic, improving execution efficiency across gaming, Al, finance, scientific, and cloud workloads.

Description

[0001] Cross-Reference to Related Applications. This international application claims priority under PCT Article 8 and the Paris Convention to U.S. Provisional Application No. 63 / 775,990, filed March 21, 2025, and to U.S. Non-Provisional Application No. 19 / 303,020, filed August 18, 2025.Any incorporation by reference of earlier applications is intended only to the extent permitted by the applicable national or regional laws during national phase entry.

[0002] Relationship Statement. The present application is related to U.S. Application No.19 / 303,020 and is a continuation-in-part thereof under U.S. practice. All essential subject matter is expressly included herein.

[0003] Title of the Invention. Systems and Methods for Adaptive Optimization and Coordination of Data Layout and Execution on Parallel Processing Architectures.

[0004] Field of the Invention. The present disclosure relates to computer systems, and more particularly to methods and systems for improving execution efficiency on parallel processors in both compiled and managed runtime environments, including graphics processing units (GPUs), tensor processing units (TPUs), neural processing units (NPUs), field-programmable gate arrays (FPGAs), multi-core central processing units (CPUs), and heterogeneous clusters.

[0005] Technical Problem & Effect — Overview. Conventional compilers and runtimes treat data representation (e.g., object, columnar, matrix) and execution strategy (e.g., scheduling order, grouping, device mapping, kernel variant) as largely independent concerns. As workload behavior and system conditions change, the absence of a unifying coordination layer causes branch divergence, uncoalesced memory access, cache thrash, latency spikes, and under-utilization across heterogeneous processors and clusters.

[0006] Technical Effect — Coordination Layer. The disclosed technology introduces an adaptive coordination layer that observes execution context (including runtime characteristics, workload composition, or external policy directives) and selectively modifies one or more of: (i) data representation (object, columnar / SoA, AoSoA, matrix, graph, tensor, or hybrids); (ii) execution strategy (scheduling order, grouping / tiling, kernel specialization, and device mapping); and (iii) resource allocation (buffering, memory locality, power / thermal envelopes).

[0007] Performance Outcomes. This coordinated reconfiguration improves parallel efficiency, memory coalescing, warp / SIMD occupancy, and end-to-end latency across GPUs, CPUs, TPUs / NPUs, FPGAs, and distributed accelerators.

[0008] Patent-Eligibility Statement. The subject matter is rooted in computer technology and improves computer functioning by coordinating data layout and execution strategy in response to machine-level conditions, achieving results unattainable by static compilers or human operators.

[0009] Background — Complex Workloads. Modern simulation and gaming workloads are increasingly complex.

[0010] Background — Parallel Processors. Parallel processors such as GPUs achieve throughput by executing groups of threads in single-instruction multiple-thread (SIMT) or single-instruction multiple-data (SIMD) fashion.

[0011] Background — Inefficiencies from AoS / OO. Workloads authored in object-oriented (00) or array-of-structures (AoS) form lead to memory divergence, uncoalesced memory access, and poor cache utilization.

[0012] Background — Static SoA Limitation. SoA layouts are known but are traditionally static (set at compile-time) and fail to adapt at runtime when divergence changes.

[0013] Background — Scheduler Limitations. Schedulers manage thread / warp-level execution but lack higher-level monitoring or layout restructuring mechanisms.

[0014] Background — Resulting Under-Utilization. The result is under-utilized execution units, wasted bandwidth, and inefficiency in high-load domains such as gaming, Al training, financial simulations, and high-performance computing (HPC).

[0015] Background — Missing Feedback Loop. Existing data-oriented designs selected at compile time, and known kernel-level schedulers, generally do not coordinate with runtime layout reorganization or with policy engines driven by hardware metrics (e.g., branch efficiency, memory coalescing) and hysteresis. The absence of a monitoring — > decision — > re-layout — > reschedule feedback loop yields persistent divergence and under-utilization as scene / state distributions change.

[0016] Background — Need for Runtime System. There is therefore a need for a runtime system that: (a) transforms workloads into SoA or matrix representations; (b) monitors runtime metrics (branch divergence, cache coalescing, warp occupancy); (c) dynamically repartitions and re-dispatches workloads; (d) supports heterogeneous hardware and distributed clusters; and (e) provides services analogous to a managed virtual machine (VM) — including JIT, code cache, and memory / buffer management — tailored to accelerators.

[0017] Background — Joint Coordination Need. There is further a need for a system that jointly coordinates: (a) transformations among multiple data representations (OO / AoS, SoA, AoSoA, matrix, graph, tensor, or hybrids); (b) selection of execution strategies (grouping / tiling, ordering, kernel specialization, device mapping); (c) monitoring of contextual information (runtime metrics, workload composition, and / or policy directives); and (d) reconfiguration during compile-time, load-time, or runtime, including cloud / distributed deployments with transactional commit semantics.

[0018] Summary — Dual Execution Modes. The invention provides a unified system for adaptive optimization of workloads on parallel processors, capable of operating in two complementary execution modes.

[0019] Compiled / SDK Mode (Static Optimization). In one embodiment, the system functions as a compiler or software development kit that converts deterministic simulation workloads (e.g., gaming, metaverse, physics, molecular, or digital-twin systems) into structure-of-arrays (SoA), array-of-structures-of-arrays (AoSoA), or matrix layouts aligned with accelerator architecture.These workloads are optimized at build time or load time and executed via precompiled kernels.

[0020] Managed Runtime / VM Mode (Dynamic Optimization). In another embodiment, the system operates as a managed execution environment — analogous to a JVM or V8engine — capable of receiving workloads in object-oriented, intermediate, or domain-specific representations (e.g., bytecode, JSON, IR). It applies just-in-time (JIT) specialization, layout transformation, and kernel caching during execution. This mode supports continuously evolving or transactional workloads such as financial order flow, Al inference, or persistent metaverse worlds.

[0021] Shared Adaptive Core. Both modes share a common adaptive optimization core comprising layout transformation, runtime monitoring, policy management, and a heterogeneous dispatcher, thereby improving throughput, reducing divergence, and stabilizing latency across devices and clusters.

[0022] Service-Level Objectives. In certain embodiments, the runtime enforces latency or frame-time budgets by selecting tile sizes, kernel variants, or device mappings that satisfy real-time constraints.

[0023] Power-Aware Scheduling. In certain embodiments, a power / thermal policy biases layout and device selection to remain within power envelopes or thermal limits.

[0024] Reliability Commit Semantics. Re-layout operations are committed via shadow buffers with transactional semantics to ensure atomicity of state updates.

[0025] Key Aspects — Overview. The invention provides a runtime system and methods for adaptive optimization of workloads on parallel processors.

[0026] Key Aspects — Data Layout Transformation. Transformations include AoS / OO -> SoA -> AoSoA (warp-aligned tiles) — > domain-aligned matrices.

[0027] Key Aspects — Runtime Monitoring. Monitoring samples hardware and software counters, including branch efficiency, warp occupancy, memory coalescing, and cache miss rates.

[0028] Key Aspects — Dynamic Adaptation. When thresholds are crossed, workloads are reorganized via compaction, re-bucketing, and re-tiling.

[0029] Key Aspects — Heterogeneous Dispatch. A dispatcher redistributes workloads across GPUs, TPUs, CPUs, NPUs, FPGAs, and clusters.

[0030] Key Aspects — Managed Runtime Facilities. Managed runtime facilities include an intermediate representation (IR), JIT kernel specialization, a kernel cache, and device buffer management.

[0031] Key Aspects — Deployment Models. Deployment models include local GPU execution, distributed GPU clusters, and cloud runtimes.

[0032] Compilation vs. Runtime Optimization. In some embodiments, deployment occurs in a compiled pipeline (SDK integration), while in others, a managed runtime (VM / transpiler) dynamically optimizes execution.

[0033] Managed Execution Environment Analogy. In some embodiments, the invention is embodied as a managed execution environment analogous to the Java Virtual Machine (JVM) or the V8 JavaScript engine, but adapted to parallel processors.

[0034] Runtime Specialization Pipeline. Such a runtime may receive high-level intermediate code (e.g., bytecode, IR), apply the transformations described herein (SoA, AoSoA, matrix tiling), and dynamically specialize kernels.

[0035] General Applicability. The invention is not limited to any single transformation pipeline but encompasses any runtime framework that adaptively optimizes workloads on parallel processors by monitoring runtime metrics and reorganizing data layout or execution scheduling.

[0036] Embodiments and Modes — Overview. The system may be implemented in two principal modes — Compiled (SDK) Mode and Managed Runtime (VM) Mode — each supporting multiple domain-specific embodiments.

[0037] Compiled / SDK Mode — Examples. Examples include: (A) Matrix- Based Game State Management (vertical) and (D) Client-Server Vector Delta Synchronization (multiplayer / cloud).

[0038] Managed Runtime / VM Mode — Examples. Examples include: (B) General Runtime Efficiency (domain-agnostic), (C) Dynamic Structure-of-Arrays Migration, and (E) Metaverse Runtime I VM Transpiler.

[0039] Financial Streams Example. In certain embodiments, transactional event streams such as financial order flow are represented as price-aligned sub-matrices and processed in microbatches with deterministic price-time commit, enabling low-latency matching and risk computation while preserving parallel efficiency.

[0040] Architecture-Aligned Performance. The approach improves performance by aligning execution with GPU / accelerator architecture, achieving contiguous memory access, reduced warp divergence, higher utilization, and faster synchronization.

[0041] Brief Description of the Drawings — Overview. FIG. 1 is a block diagram of the runtime system (transformation, scheduling, monitoring, dispatcher). FIG. 2 is a flowchart of AoS — > SoA transformation. FIG. 3 is a scheduling pipeline mapping SoA blocks to GPU warps. FIG. 4 is a dynamic adaptation feedback loop. FIG. 5 is a cloud deployment distributing workloads across GPU nodes.

[0042] Brief Description of the Drawings — OO vs. Matrix. FIG. 6 contrasts GO vs. matrix representation (nested entities vs. contiguous columns). FIG. 7 shows a translation library (OO entities — > matrix structures). FIG. 8 shows a GPU-aligned matrix transformation (aligned vs. misaligned).

[0043] Brief Description of the Drawings — Migration. FIG. 9 is a block diagram of entity migration between archetypes. FIG. 10 is a flowchart of runtime migration. FIG. 11 is a diagram of tile-level migration within an AoSoA layout.

[0044] Brief Description of the Drawings — Runtime / Metaverse. FIG. 12 is a system diagram of the Dynamic SoA Runtime Engine execution pipeline. FIG. 13 is a Metaverse Runtime I VM Transpiler pipeline. FIG. 14 is a scene-graph to SoA and domain-aligned matrix mapping. FIG. 15 is an adaptive kernel regeneration feedback loop. FIG. 16 is CPU / GPU mirroring for matrix-based game state management on clients.

[0045] Glossary — SoA. “Structure-of-Arrays (SoA)” denotes a columnar layout with one contiguous array per attribute.

[0046] Glossary — AoSoA. “Array-of-Structures-of-Arrays (AoSoA)” denotes a tiled layout where each tile holds an AoS block; tiles align to warp / SIMD width.

[0047] Glossary — Domain-Aligned Matrix. “Domain-aligned matrix” denotes a matrix reordered by predicate (e.g., state, material) to minimize divergence.

[0048] Glossary — Intermediate Representation. “Intermediate Representation (IR)” denotes an internal form (SoA, AoSoA, matrix) for profiling, transformation, and scheduling.

[0049] Glossary — Warp / SIMD Width. “Warp / SIMD width” denotes the number of parallel lanes in a unit (e.g., 32 threads per warp on NVIDIA GPUs, 64 on SIMD CPUs).

[0050] Glossary — Managed Execution Runtime. “Managed execution runtime” denotes a VM-like layer comprising data layout, monitoring, policy, JIT / code cache, and heterogeneous dispatch.

[0051] Glossary — Divergent Predicate. “Divergent predicate” denotes a boolean function over entity attributes whose non-uniform evaluation across lanes causes warp / SIMD divergence; used to define sub-arrays / sub-matrices for regrouping.

[0052] Glossary — Tile. “Tile” denotes a contiguous block of columns / rows sized as an integer multiple of a hardware lane width and optionally cache-line aligned.

[0053] Glossary — Policy Engine. “Policy engine” denotes a component storing thresholds, hysteresis / cool-down windows, and objective functions (e.g., minimizing divergence subject to memory budget).

[0054] Detailed Description — Embodiment A Overview. Embodiment A concerns matrix-based game state management.

[0055] Embodiment A — Entity Vectors. Each entity is a structured vector, e.g., E_entity = [x, y, z, t, v_x, v_y, v_z, statel, state2, ..., state_n],

[0056] Embodiment A — Global State Matrix. A collection of entity vectors is assembled into a matrix for parallel updates.

[0057] Embodiment A — GPU-Aligned Computation. Physics, Al, and collisions are processed as vectorized operations across columns.

[0058] Embodiment A — Translation Engine. OO hierarchies (e.g., player^weapon^ammo) are flattened into structured matrices.

[0059] Embodiment A — Delta Synchronization. Minimal vector deltas are transmitted between server and client, reducing bandwidth.

[0060] Embodiment A — Plugin Integration. The approach is designed as an Unreal / Unity plugin for adoption.

[0061] Embodiment A — Advantages vs. OO Loops. Unlike object-oriented update loops requiring pointer dereferencing and sequential state checks, the matrix form enables direct application of vectorized operations (e.g., collision detection as dot products, Al state transitions as sparse matrix multiplications), yielding superior cache locality and warp-level execution.

[0062] Embodiment A — Intermediate Representation. The runtime emits an IR comprising: (i) a field table (name, type, bit-width, mutability), (ii) a column map (attribute^buffer identifier, stride, alignment), (iii) a tile descriptor (tile size, padding), and (iv) a kernel signature (operation, layout, device).

[0063] Embodiment A — Runtime API. Example APIs include: register_field(name, type, flags), openjayout(spec), emit_kernel(op, layout, device), and get_metric(id, window).

[0064] FIG. 6 — OO vs. Matrix. FIG. 6 illustrates an OO entity hierarchy compared to amatrix-based representation. OO entities reference children via pointers, producing non-contiguous memory access, whereas the matrix representation arranges attributes such as position, velocity, and health into contiguous columns enabling vectorized updates.

[0065] FIG. 7 — Translation Library. FIG. 7 illustrates translation of traditional GO game code into SoA- or matrix-optimized runtime code by analyzing field declarations, object hierarchies, and access patterns to emit optimized code or IR aligned with GPU memory architecture.

[0066] FIG. 8 — GPU-Aligned Transformation. FIG. 8 illustrates that aligning matrices to warp boundaries yields coalesced memory access and improved warp efficiency relative to misaligned storage.

[0067] Embodiment A — CPU / GPU Mirroring. Gaming efficiency can be optimized by mirroring physics and rendering logic to the state-management logic through the same matrix or SoA representation, permitting near-zero- copy synchronization and consistent transformations on shared matrix views.

[0068] FIG. 16 — Cooperative Execution. FIG. 16 depicts cooperative execution between CPU and GPU under the matrix-based model, with mirrored representations maintained within shared or synchronized buffers. CPU-side updates are consumed directly by GPU kernels without deep copies, reducing synchronization overhead.

[0069] Embodiment B — Overview. Embodiment B addresses general runtime efficiency using SoA / AoSoA / matrix.

[0070] Embodiment B — Transformation Module. A transformation module converts AoS / OO workloads to SoA.

[0071] Embodiment B — Scheduling. Blocks are partitioned into warp-sized tiles and optionally AoSoA-aligned.

[0072] Embodiment B — Monitoring. Metrics include branch divergence, memory coalescing, and warp efficiency.

[0073] Embodiment B — Dispatcher. The dispatcher restructures SoA into warp-aligned sub-arrays and reorders entities by state.

[0074] Embodiment B — Dynamic Adaptation. Dynamic adaptation uses hysteresis thresholds (e.g., branch efficiency > 85%).

[0075] Embodiment B — Managed Runtime. A managed runtime includes an IR dialect, JIT kernel specialization, and a kernel cache.

[0076] Embodiment B — Shadow Buffer Swap. A double-buffer strategy ensures compute continues while re-layout occurs.

[0077] FIG. 1 — Runtime System. FIG. 1 shows a transformation module (parser and layout converter), scheduling (tile generator and warp mapper), monitoring (branch counter and cache profiler), and dispatcher (load balancer and kernel launcher) for heterogeneous accelerators.

[0078] FIG. 2 — AoS to SoA. FIG. 2 shows a pipeline converting interleaved fields (e.g., x, y, z, v_x, v_y, v_z, health) into contiguous SoA columns, then forwarding to a GPU-aligned scheduler.

[0079] FIG. 3 — Warp Scheduling. FIG. 3 depicts partitioning SoA blocks into warp-aligned tiles, mapping to GPU warps, and retrieving specialized kernels from a cache keyed by tile size.

[0080] FIG. 4 — Feedback Adaptation. FIG. 4 shows monitoring of branch divergence and cache statistics, a policy engine with thresholds and hysteresis, restructuring modules, and a dispatcher that redistributes work to compute units.

[0081] FIG. 5 — Cloud Deployment. FIG. 5 illustrates a cloud runtime with a global scheduler distributing subtasks across a GPU cluster and a synchronization layer using double-buffering.

[0082] FIG. 12 — Dynamic SoA Runtime. FIG. 12 details an input interface for AoS / OO data, a translation layer acting as a VM core, a dynamic SoA memory manager, and an execution scheduler that groups entities by domain and launches optimized kernels; outputs include physics, Al, and rendering results.

[0083] Embodiment C — Dynamic SoA Migration. The runtime supports dynamic SoA layouts in which entities migrate between columnar groups at runtime in response to workload conditions.

[0084] Embodiment C — Archetypes and IDs. Entities are represented by stable identifiers mapping to current archetype, tile, and row, enabling migration when active attributes change or when performance counters cross thresholds.

[0085] Embodiment C — Selective Copying. Migration copies only relevant columnar data, updates index metadata, and compacts affected tiles.

[0086] Embodiment C — AoSoA Tiles. In some cases, tiles are sized to cache-line or warp boundaries to enable incremental migration at tile granularity.

[0087] Embodiment C — Overhead Minimization. Overhead is minimized by selective / background migration and by overlapping compute with layout adjustment via buffer-swapping or similar techniques.

[0088] FIG. 9 — Archetype Migration. FIG. 9 depicts migration between archetypes with stable handles preserving references.

[0089] FIG. 10 — Migration Flow. FIG. 10 shows a monitoring stage, threshold checks, migration stage, index updates, and continued execution.

[0090] FIG. 11 — Tile-Level Migration. FIG. 11 shows row-level migration between source and destination tiles to incrementally rebalance layouts.

[0091] Embodiment D — Vector Delta Synchronization. Embodiment D addresses multiplayer and cloud scenarios using vector delta encoding to reduce bandwidth and latency.

[0092] Embodiment D — Client / Server Roles. Clients compute predicted states locally; the server maintains an authoritative global state matrix; minimal deltas are transmitted and applied via matrix operations.

[0093] Embodiment D — Equation Context. Applying deltas as vector additions to the global matrix enables near-instantaneous synchronization and avoids OO-style reconciliation loops.

[0094] Embodiment E — Metaverse Runtime / VM Transpiler. In some embodiments, a metaverse-oriented execution engine receives scripts or bytecode representing entity behavior, physics, or Al logic and translates them to IR aligned with SoA / matrix-tiled layouts.

[0095] Embodiment E — JIT Specialization. The runtime dynamically generates kernels specialized to the current scene topology, executing on GPUs, CPUs, or heterogeneous clusters.

[0096] Embodiment E — Adaptive Kernel Regeneration. As scene conditions evolve, the system monitors divergence and coalescing metrics and triggers re-layout and kernel regeneration to maintain real-time budgets.

[0097] Embodiment E — Deterministic Propagation. Scene updates and interactions are synchronized via vector-delta updates to ensure deterministic propagation across clients and servers.

[0098] FIG. 13 — Metaverse Pipeline. FIG. 13 shows parsing / analysis into IR, a kernel JIT compiler, a kernel cache, a scheduler across heterogeneous devices, and policy-driven regeneration.

[0099] FIG. 14 — Scene-Graph Flattening. FIG. 14 shows translation of hierarchical scene graphs into contiguous columns or matrices grouped for coalesced access.

[0100] FIG. 15 — Adaptive Loop. FIG. 15 shows a runtime monitor, policy engine with thresholds / hysteresis, a kernel specializer, and hot-swap of regenerated kernels to maintain frame-time targets.

[0101] Applications. The disclosed techniques apply to compiled workloads such as games, physics, and molecular simulations, and to runtime workloads such as finance, Al inference, and continuous metaverse simulations.

[0102] Industrial Applicability. The runtime is applicable to industrial computing systems requiring predictable high-throughput parallel processing, including gaming engines, financial risk simulation, autonomous systems, scientific modeling, and cloud inference / training clusters.

Claims

Claims1. A computer-implemented method for adaptive coordination of workload execution on a parallel processing system, comprising:receiving workload data in a first representation;executing one or more processing operations on the workload using a first execution configuration;collecting contextual information during execution, the contextual information including at least one of runtime metrics, workload composition, or policy directives;selecting, based on the contextual information, a modified combination of data representation and execution configuration; andcontinuing execution using the modified combination on one or more parallel processors.

2. A system for adaptive coordination of workload execution on a parallel processing architecture, comprising:a coordination engine configured to maintain associations between data representations and execution strategies;a monitoring subsystem configured to collect contextual information during execution; and a control subsystem configured to modify, based on said contextual information, at least one of (i) data representation, (ii) execution strategy, or (iii) device mapping, and to apply said modification during continued execution.

3. A managed execution environment configured to perform adaptive coordination of workload execution across heterogeneous processors, the environment comprising:a translation interface for converting high-level program data into accelerator-compatible layouts,a monitoring interface for observing runtime characteristics or external policy constraints, anda reconfiguration interface for modifying data layout or kernel scheduling during execution in response to said observations.

4. A system for managed runtime execution of workloads on a parallel processor, comprising:a translation engine configured to receive simulation data in object-oriented orarray-of-structures (AoS) format and to transform the data into a structure-of-arrays (SoA) / matrix layout at runtime;a memory manager configured to allocate columnar memory buffers, compress sparse fields, and maintain synchronization between CPU and GPU memory;an execution scheduler configured to group entities based on simulation domain attributes and to dispatch parallel kernels for execution; andwherein the system monitors runtime metrics including branch divergence and memory coalescing, and adaptively restructures the data layout and execution schedule in response to performance thresholds.

5. The method of claim 1 , wherein the thresholds include branch efficiency of at least 85% and global-load coalescing of at least 90%.

6. The method of claim 1, wherein workloads are redistributed across a GPU cluster in a cloud environment, and wherein double-buffered queues are used to maintain continuous execution during restructuring.

7. The method of claim 1, wherein partitioning groups entities by branch outcome, simulation state, or divergent predicate to reduce warp divergence.

8. The method of claim 1, further comprising comparing execution metrics before and after restructuring to validate efficiency gains, and applying layout transformations to a shadow buffer with commit by double-buffer swap.

9. The method of claim 1, wherein the workload comprises a game simulation state including entities and attributes of players, non-player characters, or items.

10. The method of claim 1, wherein the workload comprises a simulation selected from pharmaceutical modeling, Al training or inference pipelines, financial simulations, physics simulations, or autonomous systems.

11. The method of claim 1, further comprising offloading select CPU-side processes to a GPU, thereby enabling the CPU to handle networking, synchronization, or validation tasks.

12. The method of claim 1, wherein the dispatcher consults a kernel variant repository or just-in-time (JIT) compiler to generate kernels specialized for the current layout, tile size, and device topology, and caches the kernels keyed by operation, layout, and device.

13. The method of claim 2, wherein the matrix is partitioned into domain-aligned sub-matrices grouped by a divergent predicate.

14. The method of claim 2, wherein tile boundaries are aligned to cacheline boundaries and padded to prevent tiles from straddling multiple warps.

15. The method of claim 2, wherein workloads are redistributed across heterogeneous processors selected from GPUs, TPUs, CPUs, NPUs, or FPGAs.

16. The method of claim 2, wherein layout transition occurs only if branch efficiency remains below a policy threshold for a plurality of consecutive sampling windows.

17. The method of claim 2, further comprising comparing execution metrics before and after restructuring to validate efficiency gains and reverting to a prior layout if no improvement is achieved.

18. The method of claim 2, wherein layout transformations are applied to a shadow buffer and committed by double-buffer swap to overlap compute and re-layout operations.

19. The method of claim 1, further comprising dynamically migrating entities between different structure-of-arrays groups at runtime in response to changes in accessed attributes or performance metrics, wherein such migration includes updating a stable reference index and copying only relevant columnar fields.

20. The system of claim 3, wherein the dispatcher is further configured to migrate entities between structure-of-arrays groups at runtime based on monitored workload conditions, using stable handles and incremental columnar copying to minimize overhead.

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