Memory

Aug 22, 2023

Simplifying GPU Application Development with Heterogeneous Memory Management

Heterogeneous Memory Management (HMM) is a CUDA memory management feature that extends the simplicity and productivity of the CUDA Unified Memory programming...
16 MIN READ
Jun 27, 2022

Boosting Application Performance with GPU Memory Access Tuning

NVIDIA GPUs have enormous compute power and typically must be fed data at high speed to deploy that power. That is possible, in principle, as GPUs also have...
13 MIN READ
Jul 27, 2021

Using the NVIDIA CUDA Stream-Ordered Memory Allocator, Part 2

In part 1 of this series, we introduced new API functions, cudaMallocAsync and cudaFreeAsync, that enable memory allocation and deallocation to be...
9 MIN READ
Jul 27, 2021

Using the NVIDIA CUDA Stream-Ordered Memory Allocator, Part 1

Most CUDA developers are familiar with the cudaMalloc and cudaFree API functions to allocate GPU accessible memory. However, there has long been an obstacle...
14 MIN READ
Jul 19, 2021

Reducing Acceleration Structure Memory with NVIDIA RTXMU

Acceleration structures spatially organize geometry to accelerate ray tracing traversal performance. When you create an acceleration structure, a conservative...
11 MIN READ
May 20, 2021

Tips: Acceleration Structure Compaction

In ray tracing, more geometries can reside in the GPU memory than with the rasterization approach because rays may hit the geometries out of the view frustum....
7 MIN READ
Jan 29, 2021

Managing Memory for Acceleration Structures in DirectX Raytracing

In Microsoft Direct3D, anything that uses memory is considered a resource: textures, vertex buffers, index buffers, render targets, constant buffers, structured...
6 MIN READ
Dec 18, 2020

Making Apache Spark More Concurrent

Apache Spark provides capabilities to program entire clusters with implicit data parallelism. With Spark 3.0 and the open source RAPIDS Accelerator for Spark,...
7 MIN READ
Image depicting NVIDIA CEO Jen-Hsun Huang explaining the importance of the RAPIDS launch demo at GTC Europe 2018.
Dec 08, 2020

Fast, Flexible Allocation for NVIDIA CUDA with RAPIDS Memory Manager

When I joined the RAPIDS team in 2018, NVIDIA CUDA device memory allocation was a performance problem. RAPIDS cuDF allocates and deallocates memory at high...
24 MIN READ
Apr 15, 2020

Introducing Low-Level GPU Virtual Memory Management

There is a growing need among CUDA applications to manage memory as quickly and as efficiently as possible. Before CUDA 10.2, the number of options available to...
23 MIN READ
Aug 06, 2019

GPUDirect Storage: A Direct Path Between Storage and GPU Memory

As AI and HPC datasets continue to increase in size, the time spent loading data for a given application begins to place a strain on the total application’s...
17 MIN READ
Feb 21, 2019

Optimizing End-to-End Memory Networks Using SigOpt and GPUs

Natural language systems have become the go-between for humans and AI-assisted digital services. Digital assistants, chatbots, and automated HR systems all rely...
18 MIN READ
Unified Memory
Nov 19, 2017

Maximizing Unified Memory Performance in CUDA

Many of today's applications process large volumes of data. While GPU architectures have very fast HBM or GDDR memory, they have limited capacity. Making the...
18 MIN READ
Mar 25, 2014

NVLink, Pascal and Stacked Memory: Feeding the Appetite for Big Data

For more recent info on NVLink, check out the post, "How NVLink Will Enable Faster, Easier Multi-GPU Computing". NVIDIA GPU accelerators have emerged in...
5 MIN READ
GPU Pro Tip
Dec 04, 2013

CUDA Pro Tip: Increase Performance with Vectorized Memory Access

Many CUDA kernels are bandwidth bound, and the increasing ratio of flops to bandwidth in new hardware results in more bandwidth bound kernels. This makes it...
6 MIN READ
Unified Memory
Nov 18, 2013

Unified Memory in CUDA 6

With CUDA 6, NVIDIA introduced one of the most dramatic programming model improvements in the history of the CUDA platform, Unified Memory. In a typical PC or...
12 MIN READ