arXiv subjects

Suyash Gupta

Publications and source records attributed to Suyash Gupta.

27 records · Page 2Linked to original sources

arXiv2021

Blockchain Transaction Processing

A blockchain is an append-only linked-list of blocks, which is maintained at each participating node. Each block records a set of transactions and their associated metadata. Blockchain transactions act on the identical ledger data stored at each node. Blockchain was first perceived by Satoshi Nakamoto as a peer-to-peer digital-commodity (also known as crypto-currency) exchange system. Blockchains received traction due to their inherent property of immutability-once a block is accepted, it cannot be reverted.

cs.DB↗

arXiv2021

Proof-of-Execution: Reaching Consensus through Fault-Tolerant Speculation

Multi-party data management and blockchain systems require data sharing among participants. To provide resilient and consistent data sharing, transactions engines rely on Byzantine FaultTolerant consensus (BFT), which enables operations during failures and malicious behavior. Unfortunately, existing BFT protocols are unsuitable for high-throughput applications due to their high computational costs, high communication costs, high client latencies, and/or reliance on twin-paths and non-faulty clients. In this paper, we present the Proof-of-Execution consensus protocol (PoE) that alleviates these challenges. At the core of PoE are out-of-order processing and speculative execution, which allow PoE to execute transactions before consensus is reached among the replicas. With these techniques, PoE manages to reduce the costs of BFT in normal cases, while guaranteeing reliable consensus for clients in all cases. We envision the use of PoE in high-throughput multi-party data-management and blockchain systems. To validate this vision, we implement PoE in our efficient ResilientDB fabric and extensively evaluate PoE against several state-of-the-art BFT protocols. Our evaluation showcases that PoE achieves up-to-80% higher throughputs than existing BFT protocols in the presence of failures.

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arXiv2020

RCC: Resilient Concurrent Consensus for High-Throughput Secure Transaction Processing

Recently, we saw the emergence of consensus-based database systems that promise resilience against failures, strong data provenance, and federated data management. Typically, these fully-replicated systems are operated on top of a primary-backup consensus protocol, which limits the throughput of these systems to the capabilities of a single replica (the primary). To push throughput beyond this single-replica limit, we propose concurrent consensus. In concurrent consensus, replicas independently propose transactions, thereby reducing the influence of any single replica on performance. To put this idea in practice, we propose our RCC paradigm that can turn any primary-backup consensus protocol into a concurrent consensus protocol by running many consensus instances concurrently. RCC is designed with performance in mind and requires minimal coordination between instances. Furthermore, RCC also promises increased resilience against failures. We put the design of RCC to the test by implementing it in ResilientDB, our high-performance resilient blockchain fabric, and comparing it with state-of-the-art primary-backup consensus protocols. Our experiments show that RCC achieves up to 2.75x higher throughput than other consensus protocols and can be scaled to 91 replicas.

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arXiv2020

Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction

We develop conformal prediction methods for constructing valid predictive confidence sets in multiclass and multilabel problems without assumptions on the data generating distribution. A challenge here is that typical conformal prediction methods---which give marginal validity (coverage) guarantees---provide uneven coverage, in that they address easy examples at the expense of essentially ignoring difficult examples. By leveraging ideas from quantile regression, we build methods that always guarantee correct coverage but additionally provide (asymptotically optimal) conditional coverage for both multiclass and multilabel prediction problems. To address the potential challenge of exponentially large confidence sets in multilabel prediction, we build tree-structured classifiers that efficiently account for interactions between labels. Our methods can be bolted on top of any classification model---neural network, random forest, boosted tree---to guarantee its validity. We also provide an empirical evaluation, simultaneously providing new validation methods, that suggests the more robust coverage of our confidence sets.

stat.ML↗

arXiv2020

Permissioned Blockchain Through the Looking Glass: Architectural and Implementation Lessons Learned

Since the inception of Bitcoin, the distributed systems community has shown interest in the design of efficient blockchain systems. However, initial blockchain applications (like Bitcoin) attain very low throughput, which has promoted the design of permissioned blockchain systems. These permissioned blockchain systems employ classical Byzantine-Fault Tolerant (BFT) protocols to reach consensus. However, existing permissioned blockchain systems still attain low throughputs (of the order 10K txns/s). As a result, existing works blame this low throughput on the associated BFT protocol and expend resources in developing optimized protocols. We believe such blames only depict a one-sided story. In specific, we raise a simple question: can a well-crafted system based on a classical BFT protocol outperform a modern protocol? We show that designing such a well-crafted system is possible and illustrate that even if such a system employs a three-phase protocol, it can outperform another system utilizing a single-phase protocol. This endeavor requires us to dissect a permissioned blockchain system and highlight different factors that affect its performance. Based on our insights, we present the design of our enterprise-grade, high-throughput yielding permissioned blockchain system, ResilientDB, that employs multi-threaded deep pipelines, to balance tasks at a replica, and provides guidelines for future designs.

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arXiv2020

ResilientDB: Global Scale Resilient Blockchain Fabric

Recent developments in blockchain technology have inspired innovative new designs in resilient distributed and database systems. At their core, these blockchain applications typically use Byzantine fault-tolerant consensus protocols to maintain a common state across all replicas, even if some replicas are faulty or malicious. Unfortunately, existing consensus protocols are not designed to deal with geo-scale deployments in which many replicas spread across a geographically large area participate in consensus. To address this, we present the Geo-Scale Byzantine FaultTolerant consensus protocol (GeoBFT). GeoBFT is designed for excellent scalability by using a topological-aware grouping of replicas in local clusters, by introducing parallelization of consensus at the local level, and by minimizing communication between clusters. To validate our vision of high-performance geo-scale resilient distributed systems, we implement GeoBFT in our efficient ResilientDB permissioned blockchain fabric. We show that GeoBFT is not only sound and provides great scalability, but also outperforms state-of-the-art consensus protocols by a factor of six in geo-scale deployments.

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arXiv2019

Revisiting consensus protocols through wait-free parallelization

The recent surge of blockchain systems has renewed the interest in traditional Byzantine fault-tolerant consensus protocols. Many such consensus protocols have a primary-backup design in which an assigned replica, the primary, is responsible for coordinating the consensus protocol. Although the primary-backup design leads to relatively simple and high performance consensus protocols, it places an unreasonable burden on a good primary and allows malicious primaries to substantially affect the system performance. In this paper, we propose a protocol-agnostic approach to improve the design of primary backup consensus protocols. At the core of our approach is a novel wait-free approach of running several instances of the underlying consensus protocol in parallel. To yield a high performance parallelized design, we present coordination-free techniques to order operations across parallel instances, deal with instance failures, and assign clients to specific instances. Consequently, the design we present is able to reduce the load on individual instances and primaries, while also reducing the adverse effects of any malicious replicas.

cs.DC↗

arXiv2015

DCAFE: Dynamic load-balanced loop Chunking & Aggressive Finish Elimination for Recursive Task Parallel Programs

In this paper, we present two symbiotic optimizations to optimize recursive task parallel (RTP) programs by reducing the task creation and termination overheads. Our first optimization Aggressive Finish-Elimination (AFE) helps reduce the redundant join operations to a large extent. The second optimization Dynamic Load-Balanced loop Chunking (DLBC) extends the prior work on loop chunking to decide on the number of parallel tasks based on the number of available worker threads, at runtime. Further, we discuss the impact of exceptions on our optimizations and extend them to handle RTP programs that may throw exceptions. We implemented DCAFE (= DLBC+AFE) in the X10v2.3 compiler and tested it over a set of benchmark kernels on two different hardwares (a 16-core Intel system and a 64-core AMD system). With respect to the base X10 compiler extended with loop-chunking of Nandivada et al [Nandivada et al.(2013)Nandivada, Shirako, Zhao, and Sarkar](LC), DCAFE achieved a geometric mean speed up of 5.75x and 4.16x on the Intel and AMD system, respectively. We also present an evaluation with respect to the energy consumption on the Intel system and show that on average, compared to the LC versions, the DCAFE versions consume 71.2% less energy.

cs.DC↗

arXiv2013

IMSuite: A Benchmark Suite for Simulating Distributed Algorithms

Considering the diverse nature of real-world distributed applications that makes it hard to identify a representative subset of distributed benchmarks, we focus on their underlying distributed algorithms. We present and characterize a new kernel benchmark suite (named IMSuite) that simulates some of the classical distributed algorithms in task parallel languages. We present multiple variations of our kernels, broadly categorized under two heads: (a) varying synchronization primitives (with and without fine grain synchronization primitives); and (b) varying forms of parallelization (data parallel and recursive task parallel). Our characterization covers interesting aspects of distributed applications such as distribution of remote communication requests, number of synchronization, task creation, task termination and atomic operations. We study the behavior (execution time) of our kernels by varying the problem size, the number of compute threads, and the input configurations. We also present an involved set of input generators and output validators.

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