Dataflow Computers: Their History and Future

Hurson, Ali R.; Kavi, Krishna M.

doi:10.1002/9780470050118.ecse102

Cited by 16 publications

(7 citation statements)

References 35 publications

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“…Dataflow and computing models that display extreme parallelism are among such possibilities to be considered 86 . Dataflow computing arises from situations where storing the entire data space is impossible (which encompasses extreme data science by definition), so the algorithms that direct computation are based on the availability and flow of the data and are able to employ parallelism at a high level.…”

Section: Computer Science Ideas For Extreme Data Sciencementioning

confidence: 99%

The nonequilibrium quantum many-body problem as a paradigm for extreme data science

Freericks

Nikolić

2014

Int. J. Mod. Phys. B

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Generating big data pervades much of physics. But some problems, which we call extreme data problems, are too large to be treated within big data science. The nonequilibrium quantum many-body problem on a lattice is just such a problem, where the Hilbert space grows exponentially with system size and rapidly becomes too large to fit on any computer (and can be effectively thought of as an infinite-sized data set). Nevertheless, much progress has been made with computational methods on this problem, which serve as a paradigm for how one can approach and attack extreme data problems. In addition, viewing these physics problems from a computer-science perspective leads to new approaches that can be tried to solve them more accurately and for longer times. We review a number of these different ideas here.

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Section: Computer Science Ideas For Extreme Data Sciencementioning

confidence: 99%

The nonequilibrium quantum many-body problem as a paradigm for extreme data science

Freericks

Nikolić

2014

Int. J. Mod. Phys. B

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“…Data flow graph was first introduced in the data flow machines to describe parallel computation [27]. A data flow graph is a directed graph, G(N, E), where nodes in N represent instructions and edges in E represent data dependencies among the nodes.…”

Section: B Data Flow Graphmentioning

confidence: 99%

Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis

Wang,

Yu,

Wang

et al. 2021

Preprint

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A software vulnerability could be exploited without any visible symptoms. When no source code is available, although such silent program executions could cause very serious damage, the general problem of analyzing silent yet harmful executions is still an open problem. In this work, we propose a graph neural network (GNN) assisted data flow analysis method for spotting silent buffer overflows in execution traces. The new method combines a novel graph structure (denoted DFG+) beyond data-flow graphs, a tool to extract DFG+ from execution traces, and a modified Relational Graph Convolutional Network as the GNN model to be trained. The evaluation results show that a well-trained model can be used to analyze vulnerabilities in execution traces (of previously-unseen programs) without support of any source code. Our model achieves 94.39% accuracy on the test data, and successfully locates 29 out of 30 real-world silent buffer overflow vulnerabilities. Leveraging deep learning, the proposed method is, to our best knowledge, the first generalpurpose analysis method for silent buffer overflows. It is also the first method to spot silent buffer overflows in global variables, stack variables, or heap variables without crossing the boundary of allocated chunks.

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“…Loosely, the aforementioned topics are collected together as they all aim to allow maximum exploitation of spatial communication patterns, allowing each PE to send data directly to down-stream dataflow targets [44], [45]. Dataflow connections forming communications links are often direct register-to-register transfers mediated by a common bus (e.g., [11]- [13] and many others summarized by [46], [47]). Systolic arrays are also a form of dataflow, although with a fixed spatial communication pattern, e.g.…”

Section: Related Workmentioning

confidence: 99%

Virtual-Link: A Scalable Multi-Producer, Multi-Consumer Message Queue Architecture for Cross-Core Communication

Beard

Ekanayake

et al. 2020

Preprint

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Cross-core communication is becoming a bottleneck as the number of processing elements (PEs) increase per systemon-chip (SoC). Typical hardware solutions are often inflexible; while software solutions are flexible, they have performance scaling limitations. A key problem to solve, as we will show, is that of shared state in any software-based message queue mechanism. In this paper, we propose Virtual-Link (VL), a novel light-weight communication mechanism with hardware support to facilitate data movement between multiple data producers and consumers. VL reduces the amount of coherent shared state for synchronization, which is a key bottleneck for many approaches, to zero. VL provides further performance benefit by keeping data on the fast path (i.e., within the on-chip interconnect). VL enables directed cache-injection (stashing) between PEs on the coherence bus, reducing the latency for core-to-core communication. VL is particularly effective for fine-grain tasks on streaming data. Evaluation on a full system simulator with 6 benchmarks shows that VL achieves 2.65× speedup over state-of-the-art softwarebased communication mechanisms, and reduces memory traffic down to 34%.

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Dataflow Computers: Their History and Future

Cited by 16 publications

References 35 publications

The nonequilibrium quantum many-body problem as a paradigm for extreme data science

The nonequilibrium quantum many-body problem as a paradigm for extreme data science

Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis

Virtual-Link: A Scalable Multi-Producer, Multi-Consumer Message Queue Architecture for Cross-Core Communication

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