Abstract: Processor-to-processor communication is provided by using a non-cache-coherent disaggregated memory. The communication between a first processor and a second processor uses a pipe with three circular buffers (rings): a first ring at a first memory of a first computer that includes the first processor, a second ring at a second memory of a second computer that includes the second processor, and a third (shared) ring at the disaggregated memory that is shared by the first and second processors. The first processor uses the pipe to write a descriptor (containing the data and an ownership value) to the shared ring, and the second processor performs a polling process to determine if the ownership value corresponds to the second processor so that the second processor can act on (e.g., copy and modify) the data in the descriptor.

Abstract: In one embodiment, a method includes identifying a plurality of dyslexic users on an online social network. The plurality of dyslexic users may be identified based on content objects posted by these users over a particular time period, where the content objects may include one or more of word-level errors or sentence-level errors. A machine-learning model may be trained for text correction using a corpus of social network data, which may include at least the content objects with one or more of word-level errors or sentence-level errors, and a corresponding set of corrected content objects. A text string including one or more errors may be received from a client system associated with a first user. The text string may be transformed into a vector representation using an encoder of the machine-learning model. A corrected text string may be generated from the vector representation using a decoder of the machine-learning model.

Abstract: In one embodiment, a method includes identifying a plurality of dyslexic users on an online social network. The plurality of dyslexic users may be identified based on content objects posted by these users over a particular time period, where the content objects may include one or more of word-level errors or sentence-level errors. A machine-learning model may be trained for text correction using a corpus of social network data, which may include at least the content objects with one or more of word-level errors or sentence-level errors, and a corresponding set of corrected content objects. A text string including one or more errors may be received from a client system associated with a first user. The text string may be transformed into a vector representation using an encoder of the machine-learning model. A corrected text string may be generated from the vector representation using a decoder of the machine-learning model.