Published:
Summarizing my understanding of the transcript decoding step in 10x xenium, which also applies for the most part to other imaging based spatial transcriptomics technologies like Vizgen MERSCOPE and NanoString CosMx.
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Some notes while thinking about vectorization and its connection with CPU clock rate.
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Summarising results from my experiments in transferring data (pytorch tensors) between GPUs in a fully parallel fashion, i.e. non-blocking on host CPU, and also on the sender and receiver GPUs.
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Estimating with a small experiment, the cache line size of a cpu (Apple M1 in this case) using Python code.
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Useful post about how one can think about matrix-vector multiplication in two different ways, which can be useful in different contexts.
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Talking about different Reinforcement Learning algorithms in increasing order of complexity and how they relate to each other.
Published:
Summarising results from my experiments in transferring data (pytorch tensors) between GPUs in a fully parallel fashion, i.e. non-blocking on host CPU, and also on the sender and receiver GPUs.
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Understanding with a small experiment, the feature of pytorch of (almost) always having computations in GPU in an asynchronous fashion.
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Notes about some possible in-place operation cases with tensors in pytorch.
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Guide to thinking about what happens when one calls tensor.backward() in pytorch.
Published:
Talking about different Reinforcement Learning algorithms in increasing order of complexity and how they relate to each other.
Published:
Summarizing my understanding of the transcript decoding step in 10x xenium, which also applies for the most part to other imaging based spatial transcriptomics technologies like Vizgen MERSCOPE and NanoString CosMx.
Published:
Talking about different Reinforcement Learning algorithms in increasing order of complexity and how they relate to each other.
Published:
Useful post about how one can think about matrix-vector multiplication in two different ways, which can be useful in different contexts.
Published:
Guide to thinking about what happens when one calls tensor.backward() in pytorch.