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Sriram Sankar

Publications and source records attributed to Sriram Sankar.

13 recordsLinked to original sources

Composer 2 Technical Report

Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability to efficiently solve problems for interactive use. The model is trained in two phases: first, continued pretraining to improve the model's knowledge and latent coding ability, followed by large-scale reinforcement learning to improve end-to-end coding performance through stronger reasoning, accurate multi-step execution, and coherence on long-horizon realistic coding problems. We develop infrastructure to support training in the same Cursor harness that is used by the deployed model, with equivalent tools and structure, and use environments that match real problems closely. To measure the ability of the model on increasingly difficult tasks, we introduce a benchmark derived from real software engineering problems in large codebases including our own. Composer 2 is a frontier-level coding model and demonstrates a process for training strong domain-specialized models. On our CursorBench evaluations the model achieves a major improvement in accuracy compared to previous Composer models (61.3). On public benchmarks the model scores 61.7 on Terminal-Bench and 73.7 on SWE-bench Multilingual in our harness, comparable to state-of-the-art systems.

cs.SE

WALLABY pilot survey: Blinded by the light -- discovery of a fourth member in the ESO 179-013 system

We present new ASKAP/WALLABY HI observations of the nearby dwarf galaxy system ESO 179-013 (Kathryn's Wheel), the nearest known collisional ring galaxy, located 10 Mpc away in the Local Void. The system is composed of three previously known dwarf galaxies embedded in a large HI envelope, with a newly discovered fourth member identified through HI and radio continuum emission behind a bright foreground binary. Galaxy D exhibits the highest star formation rate in the group and deviates from the HI mass-diameter relation, suggesting it is a compact, gas-rich dwarf missed due to stellar foreground contamination. The HI data reveal for the first time an extended HI envelope around the whole system, the neutral gas counterpart of the star-forming ring and gas bridges among members, suggesting a more complex interaction history than the previously proposed collisional ring scenario. ESO 179-013 thus provides a rare opportunity to study hierarchical assembly and gas dynamics in underdense environments and demonstrates the power of blind HI surveys in identifying faint members of low-mass compact groups.

astro-ph.GA

An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking

LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed SR), a transformer-based sequential ranking model for LinkedIn Feed that replaces a DCNv2-based ranker and meets strict production constraints. We detail the modeling choices, training techniques, and serving optimizations that enable deployment at a scale of 1.2 billion members. Feed SR has been serving the majority of LinkedIn's Feed traffic for over three months and shows significant improvements in member engagement (+2.10% time spent, +3.52% like, comments, or reshares) in online A/B tests compared to the existing production model. We also describe our deployment experience with alternative sequential and LLM-based ranking architectures and why Feed SR provided the best combination of online metrics and production efficiency.

cs.IR

Hot accretion onto spiral galaxies: the origin of extended and warped HI discs

Gas accretion, hot ($\sim 10^6$ K) atmospheres, and a tilt between the rotation axes of the disc and the atmosphere are all common predictions of standard galaxy evolution theory for massive star-forming galaxies at low redshift. Using idealised hydrodynamic simulations, we demonstrate that the central regions of hot galaxy atmospheres continuously condense into cool ($\sim 10^4$ K) discs, while being replenished by an inflow from larger scales. The size and orientation of the condensed disc are determined by the angular momentum of the atmosphere, so the condensed disc is expected to often be tilted and more extended than the stellar disc. Continuous accretion from hot atmospheres can thus explain the ubiquity of extended and warped HI discs around local spirals, and also potentially provide the necessary fuel for star formation. This hot accretion scenario predicts the absence of significant HI from galaxy halos, consistent with recent 21 cm constraints on nearby spirals (the so-called `HI desert'). Moreover, our analysis indicates that observations of HI warps can be used to constrain the angular momentum, accretion rate, and gas metallicity of hot galaxy atmospheres, important parameters for disc galaxy evolution that are hard to determine by other means.

astro-ph.GA

PVF:Understanding AI Vulnerability Against SDCs

Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, the escalating complexity and heterogeneity of AI hardware systems make them increasingly susceptible to hardware faults, e.g., silent data corruptions (SDC), that can potentially corrupt model parameters. When this occurs during AI inference/servicing, it can potentially lead to incorrect or degraded model output for users, ultimately affecting the quality and reliability of AI services. In light of the escalating threat, it is crucial to address key questions: How vulnerable are AI models to parameter corruptions, and how do different components (such as modules, layers) of the models exhibit varying vulnerabilities to parameter corruptions? To systematically address this question, we propose a novel quantitative metric, Parameter Vulnerability Factor (PVF), inspired by architectural vulnerability factor (AVF) in computer architecture community, aiming to standardize the quantification of AI model vulnerability against parameter corruptions. We define a model parameter's PVF as the probability that a corruption in that particular model parameter will result in an incorrect output. In this paper, we present several use cases on applying PVF to three types of tasks/models during inference -- recommendation (DLRM), vision classification (CNN), and text classification (BERT), while presenting an in-depth vulnerability analysis on DLRM. PVF has been a critical metric used for making key error management design decisions in productionizing Meta's in-house AI chip - MTIA.

cs.CR

Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors

Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality. Despite the growing number of hardware errors observed in large-scale fleet systems where DRS are deployed, the robustness of DRS has been largely overlooked. This paper presents the first systematic study of DRS robustness against hardware errors. We develop Terrorch, a user-friendly, efficient and flexible error injection framework on top of the widely-used PyTorch. We evaluate a wide range of models and datasets and observe that the DRS robustness against hardware errors is influenced by various factors from model parameters to input characteristics. We also explore 3 error mitigation methods including algorithm based fault tolerance (ABFT), activation clipping and selective bit protection (SBP). We find that applying activation clipping can recover up to 30% of the degraded AUC-ROC score, making it a promising mitigation method.

cs.IR

PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors

Graph neural networks (GNNs) have recently emerged as a promising learning paradigm in learning graph-structured data and have demonstrated wide success across various domains such as recommendation systems, social networks, and electronic design automation (EDA). Like other deep learning (DL) methods, GNNs are being deployed in sophisticated modern hardware systems, as well as dedicated accelerators. However, despite the popularity of GNNs and the recent efforts of bringing GNNs to hardware, the fault tolerance and resilience of GNNs have generally been overlooked. Inspired by the inherent algorithmic resilience of DL methods, this paper conducts, for the first time, a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy. By developing a customized fault injection tool on top of PyTorch, we perform extensive fault injection experiments on various GNN models and application datasets. We observe that the error resilience of GNN models varies by orders of magnitude with respect to different models and application datasets. Further, we explore a low-cost error mitigation mechanism for GNN to enhance its resilience. This GNN resilience study aims to open up new directions and opportunities for future GNN accelerator design and architectural optimization.

cs.LG

Detecting silent data corruptions in the wild

Silent Errors within hardware devices occur when an internal defect manifests in a part of the circuit which does not have check logic to detect the incorrect circuit operation. The results of such a defect can range from flipping a single bit in a single data value, up to causing the software to execute the wrong instructions. Silent data corruptions (SDC) in hardware impact computational integrity for large-scale applications. Manifestations of silent errors are accelerated by datapath variations, temperature variance, and age, among other silicon factors. These errors do not leave any record or trace in system logs. As a result, silent errors stay undetected within workloads, and their effects can propagate across several services, causing problems to appear in systems far removed from the original defect. In this paper, we describe testing strategies to detect silent data corruptions within a large scale infrastructure. Given the challenging nature of the problem, we experimented with different methods for detection and mitigation. We compare and contrast two such approaches - 1. Fleetscanner (out-of-production testing) and 2. Ripple (in-production testing).We evaluate the infrastructure tradeoffs associated with the silicon testing funnel across 3+ years of production experience.

cs.AR

Probing the physicochemical properties of the Leo Ring and the Leo I group

We present an absorption line study of the physical and chemical properties of the Leo HI Ring and the Leo I Group as traced by 11 quasar sightlines spread over a 600 kpc X 800 kpc region. Using HST/COS G130/G160 archival observations as constraints, we couple cloud-by-cloud, multiphase, Bayesian ionization modeling with galaxy property information to determine the plausible origin of the absorbing gas along these sightlines. We search for absorption in the range 600 km/s - 1400 km/s consistent with the kinematics of the Leo Ring/Group. We find absorption plausibly associated with the Leo Ring towards five sightlines. Along three other sightlines, we find absorption likely to be associated with individual galaxies, intragroup gas, and/or large-scale filamentary structure. The absorption along these five sightlines is stronger in metal lines than expected from individual galaxies, indicative of multiple contributions, and of the complex kinematics of the region. We also identify three sightlines within a 7-degree X 6-degree field around the Leo Ring, along which we do not find any absorption. We find that the metallicities associated with the Leo Ring are generally high, with values between solar and several times solar. The inferred high metallicities are consistent with the origin of the ring as tidal debris from a major galaxy merger.

astro-ph.GA

Silent Data Corruptions at Scale

Silent Data Corruption (SDC) can have negative impact on large-scale infrastructure services. SDCs are not captured by error reporting mechanisms within a Central Processing Unit (CPU) and hence are not traceable at the hardware level. However, the data corruptions propagate across the stack and manifest as application-level problems. These types of errors can result in data loss and can require months of debug engineering time. In this paper, we describe common defect types observed in silicon manufacturing that leads to SDCs. We discuss a real-world example of silent data corruption within a datacenter application. We provide the debug flow followed to root-cause and triage faulty instructions within a CPU using a case study, as an illustration on how to debug this class of errors. We provide a high-level overview of the mitigations to reduce the risk of silent data corruptions within a large production fleet. In our large-scale infrastructure, we have run a vast library of silent error test scenarios across hundreds of thousands of machines in our fleet. This has resulted in hundreds of CPUs detected for these errors, showing that SDCs are a systemic issue across generations. We have monitored SDCs for a period longer than 18 months. Based on this experience, we determine that reducing silent data corruptions requires not only hardware resiliency and production detection mechanisms, but also robust fault-tolerant software architectures.

cs.AR

Physical Conditions of Five O VI Absorption Systems Towards PG $1522+101$

We present the analysis of five O VI absorbers identified across a redshift path of z $\sim (0.6 - 1.3)$ towards the background quasar PG $1522+101$ with information on five consecutive ionization stages of oxygen from O II to O VI. The combined $HST$ and $Keck$ spectra cover UV, redshifted EUV, and optical transitions from a multitude of ions spanning ionization energies in the range of $\sim (13 - 300)$ eV. Low ionization (C II, O II, Si II, Mg II) and very high ionization species (Ne VIII, Mg X) are non-detections in all the absorbers. Three of the absorbers have coverage of He I, in one of which it is a $> 3 σ$ detection. The kinematic structures of these absorbers are extracted from C IV detected in $HIRES$ spectra. The farthest absorber in our sample also contains the detections of Ne V and Ne VI. Assuming co-spatial absorbing components, the ionization models show the medium to be multiphased with small-scale density-temperature inhomogeneities that are sometimes kinematically unresolved. In two of the absorbers, there is an explicit indication of the presence of a warm gas phase ($T \gtrsim 10^5$ K) traced by O VI. In the remaining absorbers, the column densities of the ions are consistent with a non-uniform photoionized medium. The sub-solar [C/O] relative abundances inferred for the absorbers point at enrichment from massive Type II supernovae. Despite metal enrichment, the inferred wide range for [O/H] $\sim$ [$-2.1, +0.2$] amongst the absorbers along with their anti-correlation with the observed H I suggest poor small-scale mixing of metals with hydrogen in the regions surrounding galaxies and the IGM.

astro-ph.GA

Solar-Metallicity Gas in the Extended Halo of a Galaxy at $z \sim 0.12$

We present the detection and analysis of a weak low-ionization absorber at $z = 0.12122$ along the blazar sightline PG~$1424+240$, using spectroscopic data from both $HST$/COS and STIS. The absorber is a weak Mg II analogue, with incidence of weak C II and Si II, along with multi-component C IV and O VI. The low ions are tracing a dense ($n_{H} \sim 10^{-3}$ cm$^{-3}$) parsec scale cloud of solar or higher metallicity. The kinematically coincident higher ions are either from a more diffuse ($n_{H} \sim 10^{-5} - 10^{-4}$ cm$^{-3}$) photoionized phase of kiloparsec scale dimensions, or are tracing a warm (T $\sim 2 \times 10^{5}$ K) collisionally ionized transition temperature plasma layer. The absorber resides in a galaxy overdense region, with 18 luminous ($> L^*$) galaxies within a projected radius of $5$ Mpc and $750$ km s$^{-1}$ of the absorber. The multi-phase properties, high metallicity and proximity to a $1.4$ $L^*$ galaxy, at $ρ\sim 200$ kpc and $|Δv| = 11$ km s$^{-1}$ separation, favors the possibility of the absorption tracing circumgalactic gas. The absorber serves as an example of weak Mg II - O VI systems as a means to study multiphase high velocity clouds in external galaxies.

astro-ph.GA

Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, software, and tooling logs are often maintained separately, making it difficult to review the logs jointly for understanding production issues. Another challenge in reviewing the logs for identifying issues is the scale - there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability. We first explore item-sets, i.e. combinations of feature values, that could identify groups of samples with sufficient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. These algorithms were designed for frequent item-set mining and association rule learning over transactional databases. After applying them on structured logs, we select the item-sets that are most unique to the target failures based on lift. We propose pre-processing steps with the use of a large-scale real-time database and post-processing techniques and parallelism to further speed up the analysis and improve interpretability, and demonstrate that such optimization is necessary for handling large-scale production datasets. We have successfully rolled out this approach for root cause investigation purposes in a large-scale infrastructure. We also present the setup and results from multiple production use cases in this paper.

cs.DC