👉 Tract computing is a novel approach to distributed computation that leverages the collective memory and processing power of a network of interconnected nodes, often referred to as "traces." Each node in the network maintains a portion of the computation's state, including data and intermediate results, which can be shared with other nodes through a communication channel. This shared memory model allows for efficient parallel processing and reduces the need for frequent data transfers, which can be a bottleneck in traditional distributed systems. By enabling nodes to work on different parts of a problem simultaneously and share results seamlessly, tract computing can significantly enhance performance and scalability for complex computational tasks, such as large-scale simulations and machine learning. The term "tract" refers to the network of these interconnected nodes, emphasizing the collaborative and distributed nature of this computing paradigm.
