Abstract: A super home node of a first chip of a multi-chip data processing system manages coherence for both local and remote cache lines accessed by local caching agents and local cache lines accessed by caching agents of one or more second chips. Both local and remote cache lines are stored in a shared cache, and requests are stored in shared point-of-coherency queue. An entry in a snoop filter table of the super home node includes a presence vector that indicates the presence of a remote cache line at specific caching agents of the first chip or the presence of a local cache line at specific caching agents of the first chip and any caching agent of the second chip. All caching agents of the second chip are represented as a single caching agent in the presence vector.

Abstract: Described herein are systems and methods for generating an embedding—a learned representation—for an image. The embedding for the image is derived to capture visual aspects, as well as textual aspects, of the image. An encoder-decoder is trained to generate the visual representation of the image. An optical character recognition (OCR) algorithm is used to identify text/words in the image. From these words, an embedding is derived by performing an average pooling operation on pre-trained embeddings that map to the identified words. Finally, the embedding representing the visual aspects of the image is combined with the embedding representing the textual aspects of the image to generate a final embedding for the image.

Abstract: Described herein are systems and methods for generating an embedding—a learned representation—for an image. The embedding for the image is derived to capture visual aspects, as well as textual aspects, of the image. An encoder-decoder is trained to generate the visual representation of the image. An optical character recognition (OCR) algorithm is used to identify text/words in the image. From these words, an embedding is derived by performing an average pooling operation on pre-trained embeddings that map to the identified words. Finally, the embedding representing the visual aspects of the image is combined with the embedding representing the textual aspects of the image to generate a final embedding for the image.

Abstract: Described herein are techniques for using supervised machine learning to determine whether an image that has been posted to a feed of an online service includes a quotation. In some instance, supervised machine learning techniques are used to infer or predict an intent of a content poster in posting a content item to a feed of an online service. By better understanding the nature of the content being posted, various recommendations can be made during the time when an end-user is posting content, and thereafter.

Abstract: Entries in a cluster-to-caching agent map table of a data processing network identify one or more caching agents in a caching agent cluster. A snoop filter cache stores coherency information that includes coherency status information and a presence vector, where a bit position in the presence vector is associated with a caching agent cluster in the cluster-to-caching agent map table. In response to a data request, a presence vector in the snoop filter cache is accessed to identify a caching agent cluster and the map table is accessed to identify target caching agents for snoop messages. In order to reduce message traffic, snoop message are sent only to the identified targets.

Abstract: Entries in a cluster-to-caching agent map table of a data processing network identify one or more caching agents in a caching agent cluster. A snoop filter cache stores coherency information that includes coherency status information and a presence vector, where a bit position in the presence vector is associated with a caching agent cluster in the cluster-to-caching agent map table. In response to a data request, a presence vector in the snoop filter cache is accessed to identify a caching agent cluster and the map table is accessed to identify target caching agents for snoop messages. In order to reduce message traffic, snoop message are sent only to the identified targets.