Search arXiv⌕ Search

arXiv · 2604.09591

Simplicity Scales

Abstract

The dominant data interchange formats encode integers using a variable number of bytes or represent floating-point numbers as variable-length UTF-8 strings. The decoder must inspect each byte for a continuation bit or parse each character individually, producing data-dependent branches that stall modern CPU pipelines. Protocol Buffers pays this cost on every integer, field tag, and length prefix. JSON pays it on every value. We present Bebop, a serialization format where every data type uses a fixed number of bytes. A 32-bit integer is always four bytes. Decoding becomes a single memory read with no conditionals. Across 19 decode workloads, Bebop decodes 9--213$\times$ faster than Protocol Buffers. On a 1536-dimension embedding vector, Bebop decodes in 2.8 nanoseconds versus 111 nanoseconds for Protocol Buffers and 4.69 microseconds for simdjson, a 1,675$\times$ gap. On records above 64 KB, the decoder achieves 86% of peak memory bandwidth. The CPU is no longer the bottleneck. We also present a transport-agnostic RPC protocol built on the same wire format. The protocol introduces batch pipelining, where dependent cross-service calls execute in a single round trip with server-side dependency resolution. It deploys over HTTP/1.1, HTTP/2, and binary transports without proxies, removing the HTTP/2 requirement that limits gRPC on serverless platforms and in browsers.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andrew Sampson, Yuta Saito, Ronny Chan. 2026-03-04. Simplicity Scales. https://arxiv.org/abs/2604.09591

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Weave: Fine-Grained Dynamic SM Scheduling in an MoE Megakernel for Compute-Communication Overlap

Mixture-of-Experts (MoE) inference under expert parallelism (EP) turns each MoE layer into a distributed computation with costly dispatch and combine communication. State-of-the-art systems reduce this cost through communication-computation overlap, splitting the GPU's SMs for communication and computation respectively. However, this approach still leaves GPU resources wasted along two dimensions. Spatially, the best SM split is determined by each layer's routing result and varies across layers and GPUs, so fixed policies mismatch the workload and waste either NVLink bandwidth or compute throughput. Temporally, complex MoE data dependencies introduce bubbles that leave SMs idle. We present Weave, to our knowledge the first MoE overlap system that performs fine-grained dynamic SM scheduling - deciding per layer and per GPU by routing results at runtime. Once routing completes, each layer's communication and computation volumes become known; Weave exploits this predictability through a lightweight cost model running inside the persistent megakernel: a spatial scheduler partitions SMs into communication workers and computation workers to match the communication/computation throughput ratio, and a temporal scheduler coordinates the two worker groups to minimize SM idleness. On 4x H100 SXM GPUs across six mainstream MoE models, Weave achieves a 2.89x geometric-mean MoE-layer speedup and a 1.33x geometric-mean end-to-end speedup over five state-of-the-art baselines.

cs.DC↗

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

GPU acceleration is now routine across robotics, but cloud-hosted GPU continuous integration (CI) runners are expensive, resulting in severe under-testing of GPU-accelerated code. We present pytest-gpu-proof, an open-source pytest plugin offering a practical middle ground. Tests can be run on a local machine, signed with a receipt of exactly what ran and what it produced, and integrated into standard CPU CI workflows (e.g., GitHub Actions). The tool is open source and on PyPI, and we are actively integrating it across our lab's software stack.

cs.DC↗

When Fancy Eviction Fails: Rethinking Cache Replacement For LLM Prefix Reuse

Long-running LLM applications repeatedly send growing context, making prefix caching critical for reducing prefill cost. Yet prefix-cache behavior under agentic workloads remains poorly understood. We study production traces from two companies and evaluate 14 eviction algorithms across HBM-constrained and large memory-pool settings. Despite a large gap to Belady, sophisticated policies designed for traditional caches provide little benefit over LRU. The reason is structural: prefix reuse is dominated by the regular pacing of active sessions, making recency unusually predictive. Prefix caching nevertheless introduces new challenges, including heavy-tailed session footprints and highly variable miss costs as attention computation grows with sequence length. We introduce the compute-savings ratio and two offline oracles to quantify these effects. Our results show that effective prefix-cache management should retain recency as its foundation while selectively adding quick demotion for one-hit prefixes, compute-aware partial eviction for expensive misses, and capacity-dependent eviction granularity. We will release the traces and simulator to support future research.

cs.DC↗