---
title: 'PackInfer: Efficient Inference Packing'
url: https://www.emergentmind.com/topics/packinfer
type: topic
---

# PackInfer: Efficient Inference Packing

PackInfer encompasses a set of compute- and I/O-aware packing methodologies for large-scale machine learning inference, targeting both transformer-based large language model (LLM) serving and tree-ensemble deployment in resource-constrained settings. The central principle across PackInfer instantiations is elimination of redundant computation or data movement by intelligently grouping and organizing computation/data layouts based on workload heterogeneity and hardware constraints.

## 1. Motivation and Context

In modern inference serving—whether in transformer LLMs, tree ensembles, or in-memory compute (IMC) accelerators—the mismatch between model execution patterns and real-world request heterogeneity significantly impacts latency, throughput, and resource utilization. Key inefficiencies stem from:

- **Compute imbalance**: Batching variable-length sequences or requests leads to underutilization of compute units, especially when using fixed-size tiling approaches (e.g., FlashAttention kernels in LLMs), as short sequences occupy entire tiles ($\eta_i = \frac{L_i^2}{T^2}$), resulting in SM idling on long-request stragglers [2602.06072].
- **I/O imbalance**: Scattered KV cache layouts and redundant prefix reads in LLMs, or random-access loading in tree ensembles stored on disk, waste bandwidth and exacerbate latency [2602.06072, 2011.05383].
- **Memory bandwidth and capacity**: Prefilling in LLMs with high prompt-length variance wastes FLOPs and memory via excessive padding [2404.09529]; weight-loading in IMC architectures bottlenecks end-to-end energy-delay product (EDP) [2409.11437].

PackInfer addresses these challenges through workload-aware grouping, layout compaction, and online adaptation to evolving token or request distributions.

## 2. Compute- and I/O-Aware Packing in LLM Inference

PackInfer [2602.06072] for transformer-based LLMs unifies compute-aware and I/O-aware grouping within a single kernel-level framework:

- **Compute-Aware Grouping**: Requests are partitioned into $G$ groups to maximize GPU TCU utilization:
  $$
  G = \Bigl\lceil \frac{\sum_{i=1}^N L_i}{\mathcal C}\Bigr\rceil
  $$
  grouping is performed greedily, minimizing per-group load imbalance and dynamically re-grouping when $\Delta L$, the length drift, exceeds a threshold.
- **Unified Q–K Region Packing**: Within each group, all request sequences are offset-packed into contiguous blocks, enabling a single $T\times T$ attention tiling free of per-request padding. Attention scores are masked to prevent cross-request leakage, reducing tile count from $G \cdot \lceil L_{max}/T \rceil^2$ to $\lceil \sum_i L_i / T \rceil^2$.
- **I/O-Aware Grouping and KV Cache Layout**: Shared-prefix requests are identified via group-local prefix tries; common prefix KV blocks are loaded once into a contiguous buffer $\mathcal B_g$, followed by request-unique suffixes. Offsets $\mathcal O_g[i]$ record the location of each subblock, and headroom $\delta$ is preallocated per request to absorb future growth.

This combination minimizes straggler effects, saturates GPU compute, and abates memory bandwidth waste. CUDA kernel launches are tailored to group-wide Q and K spans; device memory is transformed prior to launch, optimizing I/O and smoothing memory fragmentation as generation evolves [2602.06072].

## 3. Serialization and Packed Inference for Tree Ensembles

PackInfer [2011.05383]—as instantiated with PACSET (Packed Serialized Trees)—applies external-memory algorithmic principles to optimize inference for large tree ensembles (random forests, gradient-boosted trees) when model size exceeds available memory:

- **Interleaved Bin Packing**: The top $D$ levels of $T$ trees are interleaved across disk blocks, ensuring high static locality. For block size $B$ and node size $S_n$, $D\cdot T \leq \lfloor B/S_n \rfloor$ determines packing feasibility, and the fraction of useful root/upper-level nodes per I/O is maximized.
- **Statistical Collocation**: Nodes in each residual tree (i.e., below the bin) are ordered by leaf cardinality $c(\ell)$ (number of samples visiting a given leaf), i.e. $c(n)=\sum_{ℓ\in\mathrm{Leaves}(n)}c(ℓ)$. Weighted DFS traversal ensures frequently-used root-to-leaf paths are co-located, further maximizing use per I/O.
- **Block-aware Layout**: Nodes are packed into I/O blocks via greedy, block-aligned segmentation, with each new block seeded by the unplaced node with highest $c(n)$.
- **On-demand Loading**: Deserialization during inference loads only the blocks traversed by the input—the model is never fully loaded into RAM. This technique achieves 2–6$\times$ lower latency and 50–80% fewer block loads in “larger than RAM” use-cases, with negligible DRAM footprint [2011.05383].

## 4. Efficient Prefilling and Bin-Packing for Transformer Prompts

PackInfer-inspired prepacking [2404.09529] for LLM prefilling reduces wasted computation arising from pad-token expansion:

- **Bin-Packing Algorithm**: Given $k$ prompts with lengths $l_1,\ldots,l_k$ and bin capacity $m = \max l_i$, prompts are assigned to $r \leq k$ bins such that:
  $$
  \sum_{i=1}^k x_{i,b} l_i \leq m y_b,\ \forall b
  $$
  with $x_{i,b} \in \{0,1\}$, $y_b\in\{0,1\}$. First-Fit Decreasing achieves $O(k\log k)$ assignment.
- **Attention Mask and Position Encoding Modification**: For each bin, a block-diagonal causal attention mask is synthesized to isolate each packed prompt’s tokens, and positional encodings are restarted per prompt to assure invariance of model output.
- **Single-pass KV Cache Construction**: Model forward pass is invoked with packed bins, then outputs are “unpacked” into the original $k$ prompt-aligned KV caches.
- **Efficiency Gains**: Prefilling time reduced by $1.6\times$–$3.5\times$, GPU memory by up to $60\%$, and batch-size scaling is improved up to $16\times$ before OOM. When all prompts are equal-length, benefits vanish; for extremely long inputs streaming/quadratic cost is unavoidable [2404.09529].

## 5. In-Memory Compute (IMC) Accelerators: Weight Packing and Mapping

For IMC hardware accelerators, PackInfer [2409.11437] denotes a hardware-software mapping strategy optimizing both compute throughput and weight-loading overhead:

- **Tiling and Supertiling**: Weights are partitioned into tiles $(T_i, T_o, T_m)$ and stacked into supertiles $(ST_i, ST_o, ST_m)$, respecting macro array dimensions $(D_i\times D_o\times D_h\times D_m)$.
- **Column Packing and Macro Assignment**: Supertile subsets are packed into 2D columns, maximizing density $\rho_{\rm col}$. Assignment of columns to macros obeys alignment and “one-tile-per-layer-per-macro” constraints via bin-packing. Folding (loop unrolling) is used when assignments are infeasible to shrink tile footprints temporally.
- **Energy-Delay Optimization**: Objective is
  $$
  {\rm EDP}_{\rm total} = E_{\rm total}\times T_{\rm total} = {\rm EDP}_{\rm compute+act} + {\rm EDP}_{\rm weight\_loading}
  $$
  Dominant overhead transitions from DRAM weight-loading (when $D_m=1$) to compute as $D_m$ increases. For MLPerf Tiny workloads, once all weights fit on-chip, EDP reductions reach $10\times$–$100\times$ over stacked/flattened baselines [2409.11437].
- **Schedule Generation**: PackInfer emits detailed schedules for weight loads, MAC execution, and buffer traffic, bridging network architecture and IMC fabric instantiation.

## 6. Quantitative Performance Outcomes

| Use Case                | Key Metric              | Baseline     | PackInfer/Prepacking | Gain              |
|-------------------------|------------------------|--------------|----------------------|-------------------|
| LLM Attention [2602.06072] | TBT Latency            | –            | 13–20% lower         | 1.13–1.20×        |
| LLM Attention           | Throughput             | –            | 20% higher           | 1.20×             |
| LLM Prefilling [2404.09529] | Prefill Time            | 380 ms       | 110 ms (Llama2-7B)   | 3.5×              |
| PACSET [2011.05383]     | SSD Latency            | BFS or DFS   | 2–6× lower           | 2–6×              |
| IMC Inference [2409.11437]  | EDP                     | Stacked/Flat | 10–100× lower        | 10–100×           |

PackInfer approaches are consistently most beneficial under conditions of input heterogeneity, bandwidth constraints, and limited memory.

## 7. Limitations and Extensions

PackInfer implementations exhibit certain limitations:

- **Greedy Grouping**: Heuristic grouping (e.g., longest-first binning) cannot guarantee global optimality for pathological length distributions [2602.06072].
- **Preallocated Headroom**: Additional memory is reserved to limit packing churn; aggressive suffix growth may still force repacking [2602.06072].
- **Hardware/Workload Dependency**: Tree-layout/hardware-tuning (e.g., PACSET bin/block shape, IMC packing parameters) must be matched to the target system; misconfiguration can degrade performance [2011.05383, 2409.11437].
- **Scope**: LLM Prepacking optimizes prefilling, not autoregressive generation (dynamic cache bin-packing is open) [2404.09529].
- **Single-device Designs**: Most implementations target single-GPU (LLM) or single-device IMC mapping; scalable multi-device extensions remain an open area [2602.06072].

Suggested extensions include multi-GPU group packing, sparsity-aware attention packing, dynamic tile-size selection per group, and integration with job-size-aware schedulers [2602.06072, 2409.11437].

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In conclusion, PackInfer defines a family of workload- and hardware-adaptive packing mechanisms that enable substantial improvements in inference efficiency across diverse ML settings by directly addressing compute and I/O imbalances with principled grouping, layout, and scheduling strategies [2602.06072, 2011.05383, 2404.09529, 2409.11437].

Source: https://www.emergentmind.com/topics/packinfer