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#CUDA

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♡ Eva Winterschön ♡

💻 FreeBSD CUDA drm-61-kmod 💻

"Just going to test the current pkg driver, this will only take a second...", the old refrain goes. Surely, it will not punt away an hour or so of messing about in loader.conf on this EPYC system...

- Here are some notes to back-track a botched/crashing driver kernel panic situation.
- Standard stuff, nothing new over the years here with loader prompt.
- A few directives are specific to this system, though may provide a useful general reference.
- The server has an integrated GPU in addition to nvidia pcie, so a module blacklist for the "amdgpu" driver is necessary (EPYC 4564P).

Step 1: during boot-up, "exit to loader prompt"
Step 2: set/unset the values as needed at the loader prompt

unset nvidia_load
unset nvidia_modeset_load
unset hw.nvidiadrm.modeset
set module_blacklist=amdgpu,nvidia,nvidia_modeset
set machdep.hyperthreading_intr_allowed=0
set verbose_loading=YES
set boot_verbose=YES
set acpi_dsdt_load=YES
set audit_event_load=YES
kern.consmsgbuf_size=1048576
set loader_menu_title=waffenschwester
boot

Step 3: login to standard tty shell
Step 4: edit /boot/loader.conf (and maybe .local)
Step 5: edit /etc/rc.conf (and maybe .local)
Step 6: debug the vast output from kern.consmsgbuf logs

Screenshot of an IPMI terminal, showing the FreeBSD kernel loader prompt, with variables printed, and the discussed variables being set. After this stage, the boot process resumes.
#freebsd#nvidia#cuda
Öffentlich
GripNews

🌕 GitHub - Rust-GPU/Rust-CUDA：使用 Rust 撰寫和執行快速 GPU 程式碼的生態系統
➤ 打造 Rust 在 GPU 計算領域的地位
✤ github.com/Rust-GPU/Rust-CUDA
Rust-CUDA 是一個專案，旨在使 Rust 成為使用 CUDA 工具包進行高效能 GPU 計算的首選語言。它提供了一系列函式庫和工具，可將 Rust 編譯為快速的 PTX 程式碼，並與現有的 CUDA 函式庫整合。 該專案包含 `rustc_codegen_nvvm` (Rust 編譯器後端)、`cuda_std` (GPU 端功能)、`cudnn` (深度神經網路加速)、`cust` (CPU 端 CUDA 功能)、`gpu_rand` (GPU 隨機數產生) 和 `optix` (光線追蹤) 等多個 crates，旨在覆蓋整個 CUDA 生態系統。 儘管目前仍處於早期開發階段，但 Rust-CUDA 旨在克服以往 Rust 與 CUDA 整合的困難，並充分利用 Rust 的優勢，如效能
#開發工具 #GPU #Rust #CUDA

Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust. - Rust-GPU/Rust-CUDA
GitHubGitHub - Rust-GPU/Rust-CUDA: Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust. - Rust-GPU/Rust-CUDA
Öffentlich
Amartya

My brain is absolutely fried.
Today is the last day of coursework submissions for this semester. What a hectic month.
DNN with PyTorch, Brain model parallelisation with MPI, SYCL and OpenMP offloading of percolation models,hand optimizing serial codes for performance.
Two submissions due today. Submitted one and finalising my report for the second one.
Definitely having a pint after this

#sycl#hpc#msc
Öffentlich *
Alexandre Mutel

Ported salykova.github.io/sgemm-gpu to Vulkan (nice article!) 🐢

it's 2x slower than Cuda. That one was tricky to port, (e.g. need to alias shared buffer to allow LDS/STS.128), half of it with AI, 2nd half going over lines 1-by-1

Ported the Kernel 5 from seb-v.github.io/optimization/u to Vulkan 🚀

It's only 15% slower than Cuda (and it works on AMD)

In both cases, it's quite difficult to reason about SPIR-V ➡️ ISA, apart the AMD GPU Analyzer that is helping!

Digging deeper 🏗️

salykova blog · Beating cuBLAS in Single-Precision General Matrix MultiplicationThis blog post focuses on a GPU implementation of SGEMM (Single-precision GEneral Matrix Multiply) operation defined as C := alphaAB + beta*C. We’ll review the algorithm’s design and discuss optimization techniques such as inlined PTX, asynchronous memory copies, double-buffering, avoiding shared memory bank conflicts, and efficient coalesced storage through shared memory.
#vulkan#cuda
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Paul Houle

Just got my RSS reader YOShInOn building with uv and running under WSL2 with the Cuda libraries, despite a slight version mismatch... All I gotta do is switch it from arangodb (terrible license) to postgres, and it might have a future... With sentence_transformers running under WSL2 I might even be able to deduplicate the million images in my Fraxinus image sorter

screenshot with two windows; on left there is a summary of the output of a recommendation engine, on the right there is a list of python packages used by that same engine
#python#programming#ai
Antwortete im Thread
Öffentlich
Giuseppe Bilotta

Even now, Thrust as a dependency is one of the main reason why we have a #CUDA backend, a #HIP / #ROCm backend and a pure #CPU backend in #GPUSPH, but not a #SYCL or #OneAPI backend (which would allow us to extend hardware support to #Intel GPUs). <doi.org/10.1002/cpe.8313>

This is also one of the reason why we implemented our own #BLAS routines when we introduced the semi-implicit integrator. A side-effect of this choice is that it allowed us to develop the improved #BiCGSTAB that I've had the opportunity to mention before <doi.org/10.1016/j.jcp.2022.111>. Sometimes I do wonder if it would be appropriate to “excorporate” it into its own library for general use, since it's something that would benefit others. OTOH, this one was developed specifically for GPUSPH and it's tightly integrated with the rest of it (including its support for multi-GPU), and refactoring to turn it into a library like cuBLAS is

a. too much effort
b. probably not worth it.

Again, following @eniko's original thread, it's really not that hard to roll your own, and probably less time consuming than trying to wrangle your way through an API that may or may not fit your needs.

6/

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