Accelerating Blockchain Search of Full Nodes Using GPUs

Blockchain technology has the characteristics of decentralization and tamper resistance, which can store data safely and reduce the cost of trust effectively. However, the existing blockchain system has weak performance in data management, and only supports traversal queries with transaction hashes as keywords. The query method based on the account transaction trace chain (ATTC) improves the query efficiency of historical transactions of the account. However, the efficiency of querying accounts with longer transaction chains has not been effectively improved. Given the inefficiency and single method of the ATTC index in the query, we propose a subchain-based account transaction chain (SCATC) index structure. First, the account transaction chain is divided into subchains, and the last block of each subchain is connected by a hash pointer. The block-by-block query mode in ATTC is converted to the subchain-by-subchain query mode, which shortens the query path. Multiple transactions of the same account in the same block are merged and stored, which simplifies the construction cost of the index and saves storage resources. then, the construction algorithm and query algorithm is given for the SCATC index structure. Simulation analysis shows that the SCATC index structure significantly improves query efficiency.

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“…Morishima et al [32]. propose to accelerate blockchain search through GPU using the higher computing power of GPU.…”

Section: Related Workmentioning

confidence: 99%

A blockchain index structure based on subchain query

Xing

Chen

et al. 2021

J Cloud Comp

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“…In [10], we proposed an acceleration method of Blockchain transaction search using in-GPU cache. In Blockchain systems, most users use Blockchain indirectly via services provided by full nodes, such as exchanges and wallet applications.…”

Section: Blockchain Search Using In-gpu Cachementioning

confidence: 99%

“…The Blockchain transaction search was accelerated by 3.4 times faster than existing method, which is a general purpose GPU search based on key-value store structure [11], [12]. By using the Blockchain transaction search [10] in combination with the Blockchain anomaly detection method proposed in this paper, an entire Blockchain system can be accelerated, because anomaly detection is executed at full nodes that provide the services.…”

Section: Blockchain Search Using In-gpu Cachementioning

confidence: 99%

In-GPU Cache for Acceleration of Anomaly Detection in Blockchain

Morishima

Matsutani

2020

IEICE Trans. Inf. & Syst.

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Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, anomaly detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate anomaly detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and anomaly detection in the GPU. We also propose a conditional feature extraction method to reduce computation cost of anomaly detection. We employ anomaly detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 8.6 times faster than CPU processing method and 2.6 times faster than GPU processing method that does not perform feature extraction on the GPU. In addition, the conditional feature extraction method achieves 1.7 times faster than the unconditional method when the number of users satisfying a given condition is 200 thousands out of one million.

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“…In the last decade, developers have found additional general-purpose use cases that take advantage of GPU's architectures. Developers have been using GPUs to accelerate a wide range of applications that require high performance, including large-scale physics simulation [NHKM14], medical imaging [YTZ * 08], data visualization [ME09], artificial intelligence [OJ04], and blockchain hashing [MM18]. A GPU is capable of performing thousands of calculations in parallel.…”

Section: Introductionmentioning

confidence: 99%

Daisen: A Framework for Visualizing Detailed GPU Execution

Zhang

Mosallaei

Shah

et al. 2021

Computer Graphics Forum

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Graphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator‐generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator‐generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator‐generated GPU execution traces. Daisen also includes a web‐based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open‐sourced implementation of Daisen can be found at http://gitlab.com/akita/vis. Supplemental materials including a demo video, survey questions, evaluation study guide, and post‐study evaluation survey are available at http://osf.io/j5ghq.

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Accelerating Blockchain Search of Full Nodes Using GPUs

Cited by 35 publications

References 5 publications

A blockchain index structure based on subchain query

A blockchain index structure based on subchain query

In-GPU Cache for Acceleration of Anomaly Detection in Blockchain

Daisen: A Framework for Visualizing Detailed GPU Execution

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