This is a tool for binary instrumentation. Clone the repository using the command: git clone https://github.com/dyninst/dyninst.git Some useful links: https://github.com/BinaryAnalysisPlatform/bap/ https://studylib.net/doc/5617117/dyninst-tutorial-slides http://www.paradyn.org/tutorial/
Nsight Systems puts host ranges, CUDA API calls, GPU kernels and memory operations on one timeline. It helps locate gaps in recorded GPU activity and inspect the host-side work around them.
What each of the standard building blocks actually does, why it replaced the thing before it, and what it costs.
How FlashAttention computes exact attention without materializing quadratic score and probability matrices in GPU memory.
Buffer overflow attacks Buffer overflow attack basically means that the content provided by the user to an application has a bigger size than what the application was expecting. In this case, if the application did not handle this kind of situation, then the user provided content will overwrite the stack of the application. In most cases this will corrupt the stack and the program will end with a segmentation fault. ...
What quantization actually changes in a network, which parts of a transformer break first, and which techniques repair them.
How successive FlashAttention implementations respond to the bottleneck exposed by each new GPU generation, and what decoding needs instead.
FlashInfer treats LLM attention as a serving systems problem spanning data layout, kernel generation, and runtime scheduling.
Follow a batch from the CPU to the GPU, distinguish host ranges from device activity, and use an existing trace to choose the next profiling question. A starting point for the PyTorch Profiler tutorial, Nsight Systems tutorial, and their comparison.
The scheduler is the layer between paged memory and fast kernels, and it decides whether either of them matters.
Use existing ResNet-50 traces to learn what torch.profiler and Nsight Systems explain, what NVTX bridges, and why neither a busy timeline nor an operator percentage proves a bottleneck.
How agentic orchestration and Mixture-of-Experts routing introduce conditional execution at different levels of the LLM inference stack.
Google tasks is a great tool to manage tasks. We can further extend its capabilities by tracking the tasks completed so far, which allows us to look back and see where all the time went. By connecting Google tasks to automate.io, we can add an entry to the Google calendar when a task is marked complete, and also create an entry into a Google sheet with all the details, such as name of the task, date tasks were created, and when it was completed. ...
Experiments with the CPU benchmark The iso can be downloaded from here: https://iitd-plos.github.io/col729/labs/lab0/lab0.html The link is valid only within IITD. The installation module is given at: https://www.spec.org/cpu2017/Docs/install-guide-unix.html After extracting the ISO run ./install.sh There might be a need to make the folder executable: chmod +x -R <folder name> After the installation, setup the environment by sourcing the shrc or the chrc file. source shrc go config cp Example-gcc-linux-x86.cfg try1.cfg Run using the command: ...