K-Model: A New Computational Model for Stream Processors

Capannini, Gabriele; Silvestri, Fabrizio; Baraglia, Ranieri

doi:10.1109/hpcc.2010.22

Cited by 5 publications

(6 citation statements)

References 15 publications

(18 reference statements)

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“…Efficiency of using a distributed network is provided due to the maximum load of all resources in order to increase the volume of transmitted traffic. We need to use a rational choice of paths for traffic passing through the network in order to achieve a balanced load of all network resources [15,16].…”

Section: Related Workmentioning

confidence: 99%

Effective Distribution of Tasks in Multiprocessor and Multi-Computers Distributed Homogeneous Systems

Zybin¹,

Khoroshko²,

Maksymovych³

et al. 2021

IJC

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Nowadays, a promising is the direction associated with the use of a large number of processors to solve the resource-intensive tasks. The enormous potential of multiprocessor and multicomputer systems can be fully revealed only when we apply effective methods for organizing the distribution of tasks between processors or computers. However, the problem of efficient distribution of tasks between processors and computers in similar computing systems remains relevant. Two key factors are critical and have an impact on system performance. This is load uniformity and interprocessor or intercomputer interactions. These conflicting factors must be taken into account simultaneously in the distribution of tasks in multiprocessor computing systems. A uniform loading plays a key role in achieving high parallel efficiency, especially in systems with a large number of processors or computers. Efficiency means not only the ability to obtain the result of computations in a finite number of iterations with the necessary accuracy, but also to obtain the result in the shortest possible time. The number of tasks intended for execution on each processor or each computer should be determined so that the execution time is minimal. This study offers a technique that takes into account the workload of computers and intercomputer interactions, and allows one to minimize the execution time of tasks. The technique proposed by the authors allows the comparison of different architectures of computers and computing modules. In this case, a parameter is used that characterizes the behavior of various models with a fixed number of computers, as well as a parameter that is necessary to compare the effectiveness of each computer architecture or computing module when a different number of computers are used. The number of computers can be variable at a fixed workload. The mathematical implementation of this method is based on the problem solution of the mathematical optimization or feasibility.

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Section: Related Workmentioning

confidence: 99%

Effective Distribution of Tasks in Multiprocessor and Multi-Computers Distributed Homogeneous Systems

Zybin¹,

Khoroshko²,

Maksymovych³

et al. 2021

IJC

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“…Tools for automatically converting CPU code into GPU code are now becoming popular [55] and useful for fields that use parallelism at a high level tool and not as a goal of their research. In a more theoretical level, a new GPU-based computational model has also been proposed; the K-model [16] which serves for analyzing GPU-based algorithms.…”

Section: Latest Advances and Open Problems In Gpu Computingmentioning

confidence: 99%

A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures

Navarro

Hitschfeld

Mateu

2014

Commun. comput. phys.

151

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Abstract. Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions. The evolution of computer architectures (multi-core and many-core) towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm. In the last decade, the graphics processing unit, or GPU, has gained an important place in the field of high performance computing (HPC) because of its low cost and massive parallel processing power. Super-computing has become, for the first time, available to anyone at the price of a desktop computer. In this paper, we survey the concept of parallel computing and especially GPU computing. Achieving efficient parallel algorithms for the GPU is not a trivial task, there are several technical restrictions that must be satisfied in order to achieve the expected performance. Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it. Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model. In particular, we show how this new technology can help the field of computational physics, especially when the problem is data-parallel. We present four examples of computational physics problems; n-body, collision detection, Potts model and cellular automata simulations. These examples well represent the kind of problems that are suitable for GPU computing. By understanding the GPU architecture and its massive parallelism programming model, one can overcome many of the technical limitations found along the way, design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.

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“…In a previous work we argued that minimizing the number of data-transfers is not enough (Capannini et al, 2009). In particular, in the cache-based model, proposed by Frigo, Leiserson, Prokop, & Ramachandran (1999), the bandwidth needed to replace a cache-line, in the case of cache-miss, is constant.…”

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confidence: 98%

“…K-model: a many-core stream-based computational model K-model has been designed to model all the main peculiarities of the novel generation of stream multiprocessors (Capannini et al, 2010). It is not the main goal of this paper to present in details K-model.…”

mentioning

confidence: 99%

“…In particular, in the cache-based model, proposed by Frigo, Leiserson, Prokop, & Ramachandran (1999), the bandwidth needed to replace a cache-line, in the case of cache-miss, is constant. Following the K-model rules (Capannini et al, 2010), the memory bandwidth is fully exploited when simultaneous memory accesses can be coalesced into a single memory transaction. This means that it is possible to reduce the latency of data transfer by reorganizing in the proper manner the accesses to the offchip memory.…”

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confidence: 99%

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