wrote my first CUDA, a simple c = a + b: what its doing: > allocate CPU mem and fill with input data > allocate GPU mem > copy input data CPU to GPU > gpu kernel computes > copy result back gpu to cpu > print results from CPU > free GPU mem 😭

matrix addition in CUDA, the first picture is launching 1 block of NxN threads. ~ each thread computes one element of matrix > dim3 threadsPerBlock(N,N); > MatAdd<<<1, threadsPerBlock>>>(d_A, d_B, d_C, N); this works fine with small matrices as i did N=4, which is only 16

(1/n) Building Learnflow -implemented throttling for Free/Premium Users 10/day for free and 1000 for premium(for the sake of testing) -cached monthly and weekly progress summaries -benchmark tests for cache and non-cached responses, insane diff more info below

guy is building crazy stuff

Had my fun with Mint, its amazing, caused 0 problems since install but got bored of it, Installing Fedora

fedora is amazing. no ricing yet, just setting up stuff atm

Building LearnFlow -learnt about background jobs and queues -used Celery as task queue system and Redis as the message broker -offloaded goal inactivity reminder email sending task to background through this more details below

got a new desk and monitor, so insane. loving fedora aswell

A Detailed Explanation of CUDA Thread Hierarchy (Threads, Blocks, and Grids): A CUDA thread is the smallest unit of execution on the GPU, similar to a CPU thread, but designed for massive parallelism, each thread has its own registers and runs the same kernel code independently,

i didnt backup my frameworks obsidian vault before switching to fedora, fml 😭😭

finally i can sleep, updates on my framework tmr.

only reason to use LinkedIn

update on my framework: > started implementation of Autograd Engine: the autograd engine has few key tasks: track tensor dependencies, compute gradients automatically, and backpropagate through operations. > added computation graph in the framework: forward pass works for

helpful diagram for memory hierarchy explanation:

1 away from 7 million

700! this is insanely quick, thank you guys

A short post on CUDA Programming Model: CUDA programming model, the way cuda lets developers actually write programs for massively parallel processors (hardware that runs thousands of threads at the same time) according to the NVIDIA CUDA c++ guide, cuda is built around three

made a medium account where i’ll be sharing detailed explanations about my framework. i’ll keep posting small progress updates on X, things like new features i added or quick milestones, but on medium, i’ll go deep into the actual implementation. i’ll be breaking down how each

ran a distilgpt2 model locally, tested with different prompts, tweaked generation parameters to observe variations in output. I'll share more on this tmr, gn.