2a:null 2b:["$","title",null,{"children":"Frontend Parallelization On Your GPU | Rob Sutcliffe | Observable"}] 30:T41f,This notebook is used to **benchmark running the same matrix multiplication function on your CPU and GPU** to identify how many computations need to take place before it becomes optimal to run calculations on the GPU. (*for my machine it's around 80,000 concurrent computations before the GPU becomes the optimal processor*) The matrixes are generated as part of the same function so we can test against running the same computations on a server (*without having to send huge amounts of data both ways*) with the added delay of network time. This is hidden by default as it is obviously much slower (using a standard AWS Lambda function running [this python script](https://github.com/robsutcliffe/lambda-benchmark-functions/blob/main/multiplyMatrix.py)) you can switch this on with the toggle below. *Note: the GPU can run far more concurrent computations but I caped it at half-a-million because the CPU and lambda can't* **Move the computation size side slider and click `Run Calculations`** **Run a few times to create a new scatter Plot**31:T340b,placeholderPerformanceEntries = [ { method: "GPU", durration: 27.100000023841858, size: 1, computations: 1 }, { method: "CPU", durration: 0.10000002384185791, size: 1, computations: 1 }, { method: "GPU", durration: 61.799999952316284, size: 23, computations: 529 }, { method: "CPU", durration: 0.5, size: 23, computations: 529 }, { method: "GPU", durration: 72.10000002384186, size: 56, computations: 3136 }, { method: "CPU", durration: 1.399999976158142, size: 56, computations: 3136 }, { 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