We present expository-style articles and coding tutorials on our blog.
-
CUTLASS Tutorial: Persistent Kernels and Stream-K
Welcome to Part 3 of our tutorial series on GEMM (GEneral Matrix Multiplication). In Parts 1 and 2, we discussed GEMM at length from the perspective of a single threadblock, introducing the WGMMA matmul primitive, pipelining, and warp specialization. In this part, we will examine GEMM from the perspective of the entire grid. At this […] Go to article…
-
Epilogue Fusion in CUTLASS with Epilogue Visitor Trees
Welcome to a supplemental article for our tutorial series on GEMM (GEneral Matrix Multiplication). Posts in the main series (1, 2) have discussed performant implementations of GEMM on NVIDIA GPUs by looking at the mainloop, the part responsible for the actual GEMM computation. But the mainloop is only a part of the CUTLASS workload. In […] Go to article…
-
GPU passthrough on Proxmox VE 8.2
In this guide, we will walk through the steps to enable GPU passthrough and by extension PCIe passthrough on a virtual machine (VM) deployed through Proxmox. PCIe passthrough provides a path for VMs to directly access underlying PCIe hardware, in the case of this article, an Nvidia® A30 GPU. This setup is ideal for scenarios […] Go to article…
-
CUTLASS Tutorial: Efficient GEMM kernel designs with Pipelining
Welcome to Part 2 of our tutorial series on GEMM (GEneral Matrix Multiplication). In Part 1, we discussed the computational side of GEMM by going over WGMMA, which is the primitive instruction to multiply small matrix tiles on GPUs based on the NVIDIA® Hopper™ architecture. In this part, we turn our focus to the memory […] Go to article…
-
CUTLASS Tutorial: Fast Matrix-Multiplication with WGMMA on NVIDIA® Hopper™ GPUs
No series of CUDA® tutorials is complete without a section on GEMM (GEneral Matrix Multiplication). Arguably the most important routine on modern GPUs, GEMM constitutes the majority of compute done in neural networks, large language models, and many graphics applications. Despite its ubiquity, GEMM is notoriously hard to implement efficiently. This 3-part tutorial series aims […] Go to article…
-
CUTLASS Tutorial: Mastering the NVIDIA® Tensor Memory Accelerator (TMA)
TMA (Tensor Memory Accelerator) is a new feature introduced in the NVIDIA Hopper™ architecture for doing asynchronous memory copy between a GPU’s global memory (GMEM) and the shared memory (SMEM) of its threadblocks (i.e., CTAs). Compared to prior approaches, TMA offers a number of advantages, such as (1) improving GPU utilization through facilitating warp-specialized kernel […] Go to article…
-
Sharing NVIDIA® GPUs at the System Level: Time-Sliced and MIG-Backed vGPUs
While some modern applications for GPUs aim to consume all GPU resources and even scale to multiple GPUs (deep learning training, for instance), other applications require only a fraction of GPU resources (like some deep learning inferencing) or don’t use GPUs all the time (for example, a developer working on an NVIDIA CUDA® application may […] Go to article…
-
Tutorial: Matrix Transpose in CUTLASS
The goal of this tutorial is to elicit the concepts and techniques involving memory copy when programming on NVIDIA® GPUs using CUTLASS and its core backend library CuTe. Specifically, we will study the task of matrix transpose as an illustrative example for these concepts. We choose this task because it involves no operation other than […] Go to article…
-
Installing Ubuntu 22.04 LTS over the Network on Servers with the NVIDIA® Grace Hopper™ Superchip
Grace™, NVIDIA’s first datacenter CPU, is a new choice of platform available for datacenter, CPU and HPC applications. The common property of these new NVIDIA Superchips is the Arm® architecture. This post reports on our experience provisioning the Ubuntu 22.04 LTS operating system (OS) on servers based on the NVIDIA Grace Hopper Superchip over the network. This procedure is applicable… Go to article…
-
Tutorial: Python bindings for CUDA libraries in PyTorch
PyTorch today is one of the most popular AI frameworks. Developed by Meta (then Facebook) and open-sourced in 2017, it features approachable, “pythonic” interfaces. This ease-of-use makes it especially potent for research and development, where a researcher might need to go through multiple iterations of novel AI workloads that they are developing. However, developing in […] Go to article…