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Researcher profile

K. V. Rashmi

K. V. Rashmi contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Distributed, Parallel, and Cluster ComputingInformation Theorymath.ITNetworking and Internet ArchitectureArtificial IntelligenceComputation and LanguageMachine Learning

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Published work

6 published item(s)

preprint2026arXiv

Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs

The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide range of mid-tier and older-generation GPUs that enjoy better availability and deliver comparable performance per dollar to top-tier hardware. To efficiently harness these heterogeneous resources for serving multiple LLMs concurrently, we introduce Coral, an adaptive heterogeneity-aware multi-LLM serving system. The key idea behind Coral is to jointly optimize resource allocation and the serving strategy of each model replica across all models. To keep pace with shifting throughput demand and resource availability, Coral applies a lossless two-stage decomposition that preserves joint optimality while cutting online solve time from hours to tens of seconds. Our evaluation across 6 models and 20 GPU configurations shows that Coral reduces serving cost by up to 2.79$\times$ over the best baseline, and delivers up to 2.39$\times$ higher goodput under scarce resource availability.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bandwidth Cost of Code Conversions in the Split Regime

Distributed storage systems must store large amounts of data over long periods of time. To avoid data loss due to device failures, an $[n,k]$ erasure code is used to encode $k$ data symbols into a codeword of $n$ symbols that are stored across different devices. However, device failure rates change throughout the life of the data, and tuning $n$ and $k$ according to these changes has been shown to save significant storage space. Code conversion is the process of converting multiple codewords of an initial $[n^I,k^I]$ code into codewords of a final $[n^F,k^F]$ code that decode to the same set of data symbols. In this paper, we study conversion bandwidth, defined as the total amount of data transferred between nodes during conversion. In particular, we consider the case where the initial and final codes are MDS and a single initial codeword is split into several final codewords ($k^I=λ^F k^F$ for integer $λ^F \geq 2$), called the split regime. We derive lower bounds on the conversion bandwidth in the split regime and propose constructions that significantly reduce conversion bandwidth and are optimal for certain parameters.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning-Augmented Streaming Codes are Approximately Optimal for Variable-Size Messages

Real-time streaming communication requires a high quality of service despite contending with packet loss. Streaming codes are a class of codes best suited for this setting. A key challenge for streaming codes is that they operate in an "online" setting in which the amount of data to be transmitted varies over time and is not known in advance. Mitigating the adverse effects of variability requires spreading the data that arrives at a time slot over multiple future packets, and the optimal strategy for spreading depends on the arrival pattern. Algebraic coding techniques alone are therefore insufficient for designing rate-optimal codes. We combine algebraic coding techniques with a learning-augmented algorithm for spreading to design the first approximately rate-optimal streaming codes for a range of parameter regimes that are important for practical applications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A locality-based approach for coded computation

Modern distributed computation infrastructures are often plagued by unavailabilities such as failing or slow servers. These unavailabilities adversely affect the tail latency of computation in distributed infrastructures. The simple solution of replicating computation entails significant resource overhead. Coded computation has emerged as a resource-efficient alternative, wherein multiple units of data are encoded to create parity units and the function to be computed is applied to each of these units on distinct servers. A decoder can use the available function outputs to decode the unavailable ones. Existing coded computation approaches are resource efficient only for simple variants of linear functions such as multilinear, with even the class of low degree polynomials requiring the same multiplicative overhead as replication for practically relevant straggler tolerance. In this paper, we present a new approach to model coded computation via the lens of locality of codes. We introduce a generalized notion of locality, denoted computational locality, building upon the locality of an appropriately defined code. We show that computational locality is equivalent to the required number of workers for coded computation and leverage results from the well-studied locality of codes to design coded computation schemes. We show that recent results on coded computation of multivariate polynomials can be derived using local recovering schemes for Reed-Muller codes. We present coded computation schemes for multivariate polynomials that adaptively exploit locality properties of input data-- an inadmissible technique under existing frameworks. These schemes require fewer workers than the lower bound under existing coded computation frameworks, showing that the existing multiplicative overhead on the number of servers is not fundamental for coded computation of nonlinear functions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Access-optimal Linear MDS Convertible Codes for All Parameters

In large-scale distributed storage systems, erasure codes are used to achieve fault tolerance in the face of node failures. Tuning code parameters to observed failure rates has been shown to significantly reduce storage cost. Such tuning of redundancy requires "code conversion", i.e., a change in code dimension and length on already encoded data. Convertible codes are a new class of codes designed to perform such conversions efficiently. The access cost of conversion is the number of nodes accessed during conversion. Existing literature has characterized the access cost of conversion of linear MDS convertible codes only for a specific and small subset of parameters. In this paper, we present lower bounds on the access cost of conversion of linear MDS codes for all valid parameters. Furthermore, we show that these lower bounds are tight by presenting an explicit construction for access-optimal linear MDS convertible codes for all valid parameters. En route, we show that, one of the degrees-of-freedom in the design of convertible codes that was inconsequential in the previously studied parameter regimes, turns out to be crucial when going beyond these regimes and adds to the challenge in the analysis and code construction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Bandwidth Cost of Code Conversions in Distributed Storage: Fundamental Limits and Optimal Constructions

Erasure codes have become an integral part of distributed storage systems as a tool for providing data reliability and durability under the constant threat of device failures. In such systems, an $[n, k]$ code over a finite field $\mathbb{F}_q$ encodes $k$ message symbols into $n$ codeword symbols from $\mathbb{F}_q$ which are then stored on $n$ different nodes in the system. Recent work has shown that significant savings in storage space can be obtained by tuning $n$ and $k$ to variations in device failure rates. Such a tuning necessitates code conversion: the process of converting already encoded data under an initial $[n^I, k^I]$ code to its equivalent under a final $[n^F, k^F]$ code. The default approach to conversion is to reencode data, which places significant burden on system resources. Convertible codes are a recently proposed class of codes for enabling resource-efficient conversions. Existing work on convertible codes has focused on minimizing access cost, i.e., the number of code symbols accessed during conversion. Bandwidth, which corresponds to the amount of data read and transferred, is another important resource to optimize. In this paper, we initiate the study on the fundamental limits on bandwidth used during code conversion and present constructions for bandwidth-optimal convertible codes. First, we model the code conversion problem using network information flow graphs with variable capacity edges. Second, focusing on MDS codes and an important parameter regime called the merge regime, we derive tight lower bounds on the bandwidth cost of conversion. The derived bounds show that bandwidth cost can be significantly reduced even in regimes where access cost cannot be reduced as compared to the default approach. Third, we present a new construction for MDS convertible codes which matches the proposed lower bound and is thus bandwidth-optimal during conversion.

0 citations0 reviewsNo code linkedOpen access