Coded Computation(編碼計算)到底是什麼？
Coded Computation 編碼計算 - Coded computation is an emerging paradigm for robustness in large-scale distributed computing, which applies principles from coding theory to provide robustness against slow or otherwise unavailable workers. [1] Twists are defects in the lattice that can be used to perform encoded computations. [2] Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against the workers and boosting the computation speed by efficiently mitigating stragglers. [3] Recently, coded computation has been used to reduce the completion time in distributed computing by mitigating straggler effects with erasure codes. [4] Addressing these deficiencies, in this paper, a spectral encoded computational ghost imaging technology based on orthogonal modulation model was proposed. [5] Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. [6] Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. [7] Stochastic Computing (SC) has shown great promise in achieving low hardware area and power consumption for neuromorphic architectures compared to traditional binaryencoded computation, due to its bit-serial data representation and extremely straightforward logic. [8] Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication cost. [9] We posit that a new and radically more efficient foundation for computing lies at the intersection of superconductor electronics and delay-coded computation. [10] Coded computation is a framework which provides redundancy in distributed computing systems to speed up large-scale tasks. [11] Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. [12] Recently, coded computation has emerged as a promising technique for reducing the influence of straggling computing nodes in distributed computing systems. [13] Our results extend private computation to non-linear polynomials and to data-privacy, and reveal a tight connection between private computation and coded computation. [14] We introduce a variation of coded computation that protects the data security and the master’s privacy against the workers, which is referred to as private secure coded computation. [15] Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. [16] Simulation and real experimental results show the promising performance of the proposed scheme compared with the uncoded scheme and the existing solution for coded computation over heterogeneous computing clusters. [17] Stochastic Computing (SC) is designed to minimize hardware area and power consumption compared to traditional binary-encoded computation, stemming from the bit-serial data representation and extremely straightforward logic. [18] Coded computation is an emerging research area that leverages concepts from erasure coding to mitigate the effect of stragglers (slow nodes) in distributed computation clusters, especially for matrix computation problems. [19] We address the limitations and reliability issues of serverless platforms such as straggling workers using coding theory, drawing ideas from recent literature on coded computation. [20] The captured image is a sparse coded image, which can be decoded computationally by using compressive sensing-based image reconstruction. [21] For better depiction a self-coded computational algorithm is executed rather than to move-on with build-in array. [22]編碼計算是大規模分佈式計算中穩健性的新興範式，它應用編碼理論的原則來提供針對緩慢或其他不可用工作人員的穩健性。 [1] 扭曲是晶格中的缺陷，可用於執行編碼計算。 [2] 編碼計算已被證明是分佈式矩陣乘法中的一種有效解決方案，既可以保護工作人員的隱私，又可以通過有效減少落後者來提高計算速度。 [3] 最近，編碼計算已被用於通過使用糾刪碼減輕落後效應來減少分佈式計算的完成時間。 [4] 針對這些不足，本文提出了一種基於正交調製模型的光譜編碼計算鬼成像技術。 [5] 我們的關鍵工具是編碼計算理論，它提倡通過使用糾刪碼將數據混合到計算密集型任務中，並將這些任務卸載到其他設備進行計算。 [6] 編碼計算提倡通過使用糾刪碼將數據混合到子任務中並將這些子任務卸載到其他設備進行計算，由於其更高的可靠性、更小的延遲和更低的通信成本，最近引起了人們的興趣。 [7] 與傳統的二進制編碼計算相比，隨機計算 (SC) 在實現神經形態架構的低硬件面積和功耗方面顯示出巨大的希望，因為它具有位串行數據表示和極其簡單的邏輯。 [8] 編碼計算提倡通過使用糾刪碼將數據混合到子任務中，並將這些子任務卸載到其他設備進行計算，由於其更高的可靠性、更小的延遲和更低的通信成本，最近引起了人們的興趣。 [9] 我們認為，超導電子學和延遲編碼計算的交叉點是一個新的、更高效的計算基礎。 [10] 編碼計算是一種框架，它在分佈式計算系統中提供冗餘以加速大規模任務。 [11] 我們的關鍵工具是編碼計算理論，它提倡通過使用糾刪碼將數據混合到計算密集型任務中，並將這些任務卸載到其他設備進行計算。 [12] 最近，編碼計算已經成為一種很有前途的技術，可以減少分佈式計算系統中分散計算節點的影響。 [13] 我們的結果將私有計算擴展到非線性多項式和數據隱私，並揭示了私有計算和編碼計算之間的緊密聯繫。 [14] 我們引入了一種編碼計算的變體，它可以保護數據安全和主人對工人的隱私，這被稱為私有安全編碼計算。 [15] 我們的關鍵工具是編碼計算理論，它提倡通過使用糾刪碼將數據混合到計算密集型任務中，並將這些任務卸載到其他設備進行計算。 [16] 仿真和實際實驗結果表明，與未編碼方案和現有異構計算集群上的編碼計算解決方案相比，該方案具有良好的性能。 [17] 與傳統的二進制編碼計算相比，隨機計算 (SC) 旨在最大限度地減少硬件面積和功耗，這源於比特串行數據表示和極其簡單的邏輯。 [18] 編碼計算是一個新興的研究領域，它利用擦除編碼的概念來減輕分佈式計算集群中落後者（慢節點）的影響，特別是對於矩陣計算問題。 [19] 我們解決了無服務器平台的局限性和可靠性問題，例如使用編碼理論的散亂工人，從最近關於編碼計算的文獻中汲取思想。 [20] 捕獲的圖像是稀疏編碼圖像，可以通過使用基於壓縮感知的圖像重建進行計算解碼。 [21] 為了更好地描述，執行自編碼計算算法，而不是繼續使用內置數組。 [22]
Novel Coded Computation 新穎的編碼計算
To address these challenges, in this paper, we introduce a novel coded computation scheme based on multi-agent reinforcement learning (MARL), which has many promising features such as adaptability to network changes, high efficiency and robustness to uncertain system disturbances, consideration of node heterogeneity, and decentralized load allocation. [1] We present a novel coded computation approach that leverages the properties of circulant permutation and rotation matrices. [2] This study presents a novel coded computation technique for distributed matrix-matrix product computation at a massive scale that outperforms well known previous strategies in terms of total execution time. [3]為了應對這些挑戰，在本文中，我們介紹了一種基於多智能體強化學習（MARL）的新型編碼計算方案，該方案具有對網絡變化的適應性、高效性和對不確定係統擾動的魯棒性、考慮到節點異構性和分散的負載分配。 [1] 我們提出了一種新穎的編碼計算方法，該方法利用了循環置換和旋轉矩陣的特性。 [2] 本研究提出了一種新穎的編碼計算技術，用於大規模分佈式矩陣-矩陣乘積計算，在總執行時間方面優於眾所周知的先前策略。 [3]
coded computation technique
This study presents a novel coded computation technique for distributed matrix-matrix product computation at a massive scale that outperforms well known previous strategies in terms of total execution time. [1] Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. [2] Coded computation techniques leverage coding theory to inject computational redundancy and mitigate stragglers in distributed computations. [3]本研究提出了一種新穎的編碼計算技術，用於大規模分佈式矩陣-矩陣乘積計算，在總執行時間方面優於眾所周知的先前策略。 [1] 編碼計算技術提供了針對分佈式計算中分散服務器的魯棒性，但具有以下限制：首先，它們增加了解碼複雜性。 [2] 編碼計算技術利用編碼理論來注入計算冗餘並減少分佈式計算中的落後者。 [3]