Bag of tasks 原理

本文探讨了Bagoftasks调度机制,一种适用于并行计算环境中独立任务集合的管理方式。该机制在网格计算和异构平台上有广泛应用。文章提出了使用条件,并就其起源和应用场景提出疑问。

    最近看Bag of tasks ,第一反应是baidu,google。居然没太多人介绍,至少我没找到。我就想补补这个中文空白。今天用scholar.google搜了下,很失望的是没找到Bag of tasks 的提出者,即没找到这个概念的“树根”。有点小失望。只粗略地看了其“枝叶”状态。

   Bag of tasks 其实是一个scheduler机制,在grid computing,heterogeneous platforms均有很大的应用。

   他的使用条件:

      1,A bunch of tasks to do, 

      2,tasks are all independent,

      3,similar, maybe just different input files。


我有几个问题:

1,    Bag of tasks 提出者是谁?那篇文章?



2,   是不是主要用于理论研究?



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### Bag of Words Algorithm Datasets For training or testing purposes with the Bag of Words (BoW) model, several well-known datasets provide rich resources suitable for various applications such as image classification, object recognition, and scene categorization. These datasets offer diverse collections of images along with annotations that facilitate constructing robust BoW models. #### Commonly Used Image Databases 1. **Caltech-101/256** This dataset contains around 9K labeled examples across 101 categories plus an additional clutter class in Caltech-256[^1]. The variety within these classes allows researchers to evaluate how effectively different configurations of the BoW approach handle varying levels of intra-class diversity. 2. **Pascal VOC Series** Pascal Visual Object Classes Challenge provides multiple editions like VOC2007, VOC2012 which include annotated objects belonging to twenty distinct types per picture. Such detailed labeling supports more sophisticated analyses beyond simple presence detection into aspects like pose estimation or segmentation tasks[^2]. 3. **CIFAR-10/CIFAR-100** Comprising tiny colored photos split between ten primary labels in CIFAR-10 version; alternatively expanded out over hundred finer-grained subclasses through its bigger sibling set – CIFAR-100. Despite lower resolution compared to others mentioned earlier, it remains popular due partly because faster processing times allow quicker experimentation cycles during development phases. 4. **ImageNet ILSVRC** As perhaps the largest single source available today containing millions upon millions of URLs pointing towards JPEG files spread amongst thousands upon thousands unique synsets organized hierarchically according WordNet structure. Specifically designed challenges based off subsets here have driven much advancement forward regarding deep learning techniques applied toward computer vision problems including those involving bag-of-feature representations derived via local descriptors aggregated together forming global feature vectors representative enough yet compact sufficient for efficient comparison operations against other samples encountered later down line when deployed operationally outside lab conditions. To implement a complete workflow efficiently using any chosen corpus above: ```python import numpy as np from sklearn.cluster import MiniBatchKMeans from skimage.feature import hog from sklearn.preprocessing import StandardScaler def extract_features(image_paths): """Extract HOG features from given list of image paths.""" features = [] for path in image_paths: img = load_image(path) fd = hog(img, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=False) features.append(fd) return np.array(features) # Example usage image_paths = [...] # List of filepaths leading to individual items inside selected collection features = extract_features(image_paths) scaler = StandardScaler().fit(features) scaled_features = scaler.transform(features) kmeans = MiniBatchKMeans(n_clusters=vocab_size, batch_size=batch_size).fit(scaled_features) histograms = construct_histograms(kmeans.labels_) ```
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