Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.34818

Accelerator Choice Is Not Enough: AlphaFold2 Inference on Cloud TPUs

Lorenzo Pazienza·Ihab El Bani
View original source ↗Read source document ↗Export metadata ↓

Abstract

AlphaFold2 is written in JAX, so the same inference code compiles and runs unchanged on CPUs, GPUs and Google Cloud TPUs. That portability makes the accelerator look like the main decision a user has to make. We show that it is not. Running one AlphaFold2 inference workload across a Colab CPU runtime, an NVIDIA T4 GPU and a dedicated eight-chip Cloud TPU v5e slice, we find a large hardware advantage for the TPU, 0.47 s per call in steady state on a single chip against 13.1 s on the T4 in the same measurement campaign, and three ways in which the software layer decides how much of it a user actually gets. The default execution path uses one chip of the eight, and at list prices the idle capacity makes the slice cost about as much per prediction as the GPU. Batching with jax.vmap never exceeds single-query throughput, while mapping queries across chips with jax.pmap gives eight chips 6.5-7.9x the throughput of one on a matched grid; automatic sharding leaves the per-chip footprint unchanged, consistent with replication, most plausibly because AlphaFold2 carries no sharding annotations. Our retained trace analysis of a first call at a new input shape reports about three quarters of the traced span in JAX tracing and compilation rather than execution. Reruns five weeks later reproduced neither cloud baseline, the GPU one off by roughly a factor of two, so the hardware ratio above is specific to one campaign.

Explore related subjects

cs.DCcs.LGcs.PF

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lorenzo Pazienza, Ihab El Bani. 2026-09-28. Accelerator Choice Is Not Enough: AlphaFold2 Inference on Cloud TPUs. https://arxiv.org/abs/2609.34818

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

KEEP EXPLORING

Related papers

arXiv2026

TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications

Large Language Models (LLMs) are increasingly deployed in complex multi-agent applications that rely on external function calls. This workload creates severe performance challenges for the KV Cache: spatial contention leads to the eviction of critical agents' caches and temporal underutilization leaves the cache of agents stalled on long-running function calls idling in GPU memory. We present TokenCake, a KV-Cache-centric serving framework that bridges this gap by co-optimizing scheduling and memory management through an agent-aware design. TokenCake's Temporal Scheduler employs an event-driven, opportunistic policy to proactively offload idle KV Caches during function calls and uses predictive uploading to hide data transfer latency. TokenCake's Spatial Scheduler uses dynamic memory partitioning, guided by a hybrid priority metric combining graph structure and runtime state, to reserve GPU memory for critical-path agents. Our evaluation on representative multi-agent benchmarks shows that TokenCake reduces end-to-end latency by up to 47.06% in memory-constrained settings and improves effective GPU utilization by 16.9 percentage points compared to vLLM. TokenCake is publicly available at https://github.com/pkulemonade/TokenCake.

cs.DC↗
arXiv2026

SpSYRK: Half the Work in Distributed Sparse Matrix Multiplication

The symmetric rank-$k$ update (SYRK), $C = AA^\top$, computes the dot product of each pair of rows of $A$, producing the Gram matrix $C$. Its sparse variant underpins similarity search in machine learning, graph analytics, and genomics, including Jaccard similarity on datasets too large for a single node. Despite the symmetry in its inputs and outputs, existing distributed sparse matrix multiplication algorithms such as Sparse SUMMA treat sparse SYRK as generic multiplication, computing the full output and materializing the explicit transpose even when the calling application uses only one triangle. Prior distributed $AA^\top$ computations in similarity search and genome assembly inherit this overhead from the underlying SpGEMM. This paper presents SpSYRK and CommSpSYRK, two distributed sparse SYRK algorithms that exploit symmetry. The first, SpSYRK, partitions the off-diagonal blocks of the output between the upper and lower triangular regions of the process grid and computes only the lower-triangular part of each diagonal block, halving per-process computation compared with state-of-the-art distributed SpGEMM. The second, CommSpSYRK, further reorders communication to avoid forming $A^\top$, which reduces per-process communication volume. On 32 nodes of the Perlmutter supercomputer, SpSYRK achieves a 2$\times$ speedup over an optimized Sparse SUMMA on matrices where local multiplication dominates the runtime; the advantage narrows on communication-bound inputs, a dependence that the cost model predicts from the arithmetic intensity. CommSpSYRK fixes this and consistently achieves superior scaling at high process counts. The approach is a drop-in replacement for any application computing $C = AA^\top$ via a distributed SpGEMM routine, and its triangular output can be consumed directly by subsequent operations, reducing both computation and memory footprint.

cs.DC↗
arXiv2026

Brain API: An Intent-Aware Control Plane for Policy-Governed Agentic Systems

Contemporary cloud and distributed systems expose control through resource-centric abstractions: services, deployments, network flows, execution graphs. Agentic and tool-augmented systems have meanwhile shifted application logic toward intent-driven, adaptive execution. Existing control planes, workflow engines and service meshes lack abstractions for intent-level decision governance: they cannot represent high-level goals as first-class control objects, cannot enforce policy over the mapping from intent to execution plan, and cannot produce auditable records of why one execution path was chosen over its alternatives. Control logic is therefore embedded in application code, leaving systems brittle, opaque and hard to govern. We propose Brain API, an intent-aware control plane for policy-governed agentic systems. Its central contribution is the decision artifact: a durable, versioned, auditable record of how an intent became an executable plan, capturing which policies applied, which capabilities were evaluated, which alternatives were rejected, and why. A motivating use case is agentic datasets: datasets participating as policy-governed capabilities under residency, compliance and cost constraints. We evaluate a prototype of the decision layer against two external policy corpora we did not author. On the OPA Gatekeeper constraint library it agrees with the library's own published verdicts on 42 of 42 encodable cases, 19 admit and 23 deny. On Cedar example policies, labeled by differential testing against its reference implementation, a deliberately dissimilar domain exposed three defects in our model, including a default-allow assumption that would have inverted every authorization policy. The evaluation covers policy filtering and selection; candidate generation, context signals, ranking and plan synthesis are not measured, nor is decision latency under load.

cs.DC↗