Parallelize with Goroutines

[thejhh]

Optimize the implementation to use multiple CPU cores via goroutines. Identify the most computationally heavy parts (e.g. the big matrix multiplications in BitLinear layers and the attention score computations) and split those tasks across goroutines. For example, in a BitLinear, you can partition the output neurons into chunks and have each goroutine compute the dot-products for a subset of outputs (each thread working on different rows of the weight matrix). Similarly, you can parallelize the attention computation by splitting the heads among goroutines or splitting the sequence length for the softmax and value-weight multiplications. Use synchronization (such as sync.WaitGroup) to launch these goroutines and wait for completion, combining their results into the final tensor. Aim to keep the goroutines non-blocking in terms of the main thread – launch them and then combine results once done. The official BitNet inference code allows a configurable number of threads
github.com
, so design your code also to scale with the number of available cores. Careful memory management is needed to avoid race conditions (e.g., each goroutine writes to its own portion of an output slice). By the end of this step, the model should be capable of utilizing all CPU cores, significantly accelerating inference.