Using ltrace to see what ipmctl and ndctl are doing

Occasionally, it is necessary to debug commands that are slow. Or you may simply be interested in learning how the tools work. While there are many strategies, here are some simple methods that show code flow and timing information.

To show a high-level view of where the time is being spent within libipmctl, use:

# ltrace -c -o ltrace_library_count.out -l '*ipmctl*' ipmctl show -memoryresources

To show a high-level view of where the time is being spent within libndctl, use:

# ltrace -c -o ltrace_library_count.out -l '*ndctl*' ipmctl show -memoryresources

To show a high-level view of where the time is being spent within libipmctl and libipmctl, use:

# ltrace -c -o ltrace_library_count.out -l '*ipmctl*' -l '*ndctl*' ipmctl show -memoryresources

To trace all libipmctl and libndctl functions, use:

ltrace -l '*ndctl*' -l '*ipmctl*' ipmctl version

To include the time spent within each function, use:

ltrace -T -l '*ndctl*' -l '*ipmctl*' ipmctl version

Flame graphs can be very useful. See http://www.brendangregg.com/flamegraphs.html .

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How Much RAM Could a Vector Database Use If a Vector Database Could Use RAM

Featured image generated by ChatGPT 4o model: “a low poly woodchuck by a serene lake, surrounded by mountains and a forest with tree leaves made from DDR memory modules. The woodchuck is munching on a memory DIMM. The only memory DIMM in the image should be the one being eaten.”

How Much RAM Could a Vector Database Use If a Vector Database Could Use RAM?

Although the title is a punn from the famous “woodchuck rhyme,” the question is serious for LLM applications using vector databases. As large language models (LLMs) continue to evolve, leveraging vector databases to store and search embeddings is critical. Understanding the memory usage of these systems is essential for maintaining performance, response times, and ensuring system scalability.

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The NVIDIA Developer Program provides access to a wide catalog of AI models through NVIDIA Inference Microservices (NIM), offering an OpenAI-compatible API. You can browse and discover available models at build.nvidia.com/explore/discover .

If you want to find models with free hosted endpoints in the browser, you can enable the “Free Endpoint” filter on the model catalog page. But what if you need that information programmatically – in a script, a CI pipeline, or as part of an automated workflow? The browser filter is not accessible through the API, and the /v1/models endpoint does not distinguish between free hosted models and everything else.

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Linux Kernel 6.9 is Released: This is What's New for Compute Express Link (CXL)

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The Linux Kernel 6.9 release brings several improvements and additions related to Compute Express Link (CXL) technology.

New Features

Here is a list of new features for CXL:

Here is the detailed list of all commits merged into the 6.9 Kernel for CXL and DAX. This list was generated by the Linux Kernel CXL Feature Tracker .

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