GPU memory refers to the on-chip memory available with Graphics Processing Units (GPUs) for storing transient data buffers. This data helps in complex mathematical, graphical and visual data operations. Often a GPU device must hold enormous data volumes within its own memory space prior to instruction execution.

Inadequacy of GPU memory resources may result in performance bottlenecks or unnecessary delays while the system shuffles small information packets from the CPU/global memory to GPU memory.

GPU memory is a dedicated memory space, separate from the system’s RAM. Like in all computation systems, in GPUs too the on-chip memory plays a substantial role in storing and accessing data and processes for a short time.

However, these intermittent storage requirements are often neglected when working with humongous heavy-duty workloads such as Artificial Intelligence/ Machine Learning models. Memory usage and memory bandwidth are, in fact, the most overlooked aspects of GPU resource utilization.

This article will talk about GPU memory types and the difference between CPU memory and GPU memory. We will also highlight how GPU memory availability impacts various applications and why we need high memory bandwidth for Machine Learning applications.

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Significance of GPU Memory

Also known as Video Random Access Memory (VRAM), GPU memory enables the GPU to quickly reference massive datasets and process complicated resource-intensive tasks without overloading the system’s RAM and slowing the overall performance.

VRAM is widely acknowledged for its prowess handling high-bandwidth data-intensive workloads such as 3D-rendering, video playback, Graph Neural Networks (GNNs), gaming and blockchain calculations. Not having enough VRAM memory bandwidth can cause debilitating performance issues in computing systems.

The global High-Bandwidth Memory market (HBM) is projected to reach approx. USD 5 billion by 2031, growing at CAGR 33% in the next decade 2021-31. This includes memory loaded onto GPU, CPU, APU, FPGA and ASIC devices.

Such tremendous market growth demonstrates the significant role that memory resources will continue to play in supporting computation workloads across industries.

Enterprises looking for GPU-accelerated Artificial Intelligence/ Machine Learning, Image Processing, Deep Neural Network, or other resource-intensive operations must consider memory bandwidth as a significant factor when selecting GPU resources for deployment. On-chip memory must be factored in as an important primary consideration, alongside GPU cores and dynamic partitioning possibilities.

Different Types of GPU Memory

Memory has always been a critical technology, enabling relentless advancements in various computation sectors. Whether we talk about Big Data analytics, AI/ ML/ IoT-based industrial technologies, or consumer-grade electronics like powerful smartphones, efficient memory utilization remains a dealbreaker across the spectrum.

There are different types of memories associated with GPU. These are

  1. Register memory: Fast on-chip memory that stores operands used by GPU threads. This is the fastest memory available to a GPU and is only accessible to the threads. It has the lifetime of a thread.
  2. Shared memory: This memory type is invoked when the GPU runs out of VRAM availability. These CUDA memory spaces are shared by multiple threads within a GPU block while they handle resource-intensive tasks. Shared memory has the lifetime of the block in which it was created.
  3. Local memory: The OS kernel can also allocate GPU static memory. According to the CUDA programming approach, such memory is local to the operation and is only accessible by the thread to which it is assigned. It is significantly slow via-a-vis register or shared memory.

CPU Memory vs GPU Memory

Central Processing Unit (CPU) and Graphical Processing Units (GPU) both leverage memory resources to achieve their tasks and effectively fetch data for computation.

At the heart of every computer lies the CPU. It is a generalized processing unit that handles the operating system and general tasks such as firewalls and web access. Thus, the memory it uses is also a generalized one (System RAM).

GPUs are specialized devices that handle complex, resource-intensive operations. As such, its numerous processing cores have access to dedicated VRAM to handle identical repetitive calculations.

Here is a list of differences between CPU memory and GPU memory –

CPU Memory 

GPU Memory 

System RAM, as the name suggests, specifically handles all the data associated with the system’s core operations.  Dedicated VRAM, as the name suggests, is meant for specialized purposes, such as video rendering, image-data processing and manipulation, and massive-scale dataset transmission for parallel processing. 
The consumption of CPU memory is more compared to GPU memory. It is because the CPU handles OS tasks and related operations, including GPU management.  GPU handles task-specific operations only, and therefore requires substantially less memory resources. 
CPU memory is a collection of RAM, cache, and registers working in tandem. They have a short-width interface for data movement.  GPU memory refers specifically to on-chip storage resources. They have a broad interface & shorter paths with a point-to-point connection. 
When a CPU works with its system memory, it focuses on delivering low latency.  When a GPU works with its dedicated memory, it focuses on delivering high throughput. 
CPU memory bandwidth is slower compared to GPU memory bandwidth.  GPU memory bandwidth is faster compared to CPU memory bandwidth. 

Companies tune CPU cores for handling complex operations. The data flows through a series of L1, L2, and L3 caches, followed by RAM to perform complex tasks serially. However, GPU cores are less powerful and dedicated to performing straightforward tasks.

But GPU does powerful computations by pulling data from memory and performing parallel calculations. Due to the wider bandwidth in GPU memory, it can process data at much higher volume.

Also Read: How to Find Best GPU for Deep Learning

GPU Memory Bandwidth’s Impact on Workloads

The memory bandwidth in GPU determines how fast it can transfer data to and from between processing cores and memory. We can measure this by data transmission speed between memory and computation cores or via the number of links (in the form of buses) connecting these two parts.

Memory bandwidth in GPUs impacts various tasks, be it enhancing computational productivity or running applications for healthcare or gaming.

  1. Enhance productivity: Professional workloads and engineering tasks accomplished using tools like AutoCAD and Autodesk 3ds Max demand robust systems to handle design processing and graphics-rich model development. A GPU with more memory can hold a larger data cluster for processing. Furthermore, the better the memory bandwidth, the more swiftly it will process the stored data.
  2. Streamlines gaming UX: Cloud servers that host online games must be backed by powerful GPUs that do not lag. Apart from components like CPU and RAM, the GPU memory and its bandwidth play a significant role in the overall performance and display of online games. GPU bandwidth impact gaming because what you see on the screen is directly manifested by the GPU resources.
  3. Automotive industry: Automotive industries are developing driverless cars that source real-time images from multiple directions. These driverless vehicles learn and adapt to a broad range of real-world scenarios. For handling such unmatched image recognition potential, these systems require extensive processing capabilities backed by colossal memory resources and memory bandwidth to handle such multidirectional video data flows.
  4. Healthcare and life science systems: For flawlessly generating and handling medical images from different healthcare equipment, medical systems require GPU resources with high memory bandwidth. These systems can effortlessly crunch medical record data for better insights.

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Need for High Bandwidth Memory for Resource-intensive Tasks

Even though we have top-of-the-line advanced GPUs with thousands of cores, we often encounter system bottlenecks.

Why?

It is simply because dealing with resource-intensive tasks is not always about the number of dedicated processing cores available. Factors like memory bandwidth also play a substantial role.

Imagine a situation where your GPU has thousands of sophisticated CUDA and Tensor cores, but they are all stalled waiting for the memory resources to free up from ongoing processes! Having processing cores sitting idle is a waste of time and resources!

Here is a list of some powerful Nvidia GPUs and their memory bandwidths –

GPU 

VRAM  Memory interface width (no. of links connecting the VRAM to the processor cores) 

Memory Bandwidth 

V100 

32GB HBM2 

4096-bit 

900 GB/s 

A5000 

24GB GDDR6 

384-bit 

768 GB/s 

A6000 

48GB GDDR6 

384-bit 

768 GB/s 

A4000 

16GB GDDR6 

256-bit 

448 GB/s 

A30 

16GB GDDR5 

256-bit 

933 GB/s 

A100 

80GB HBM2 

5120-bit 

1555 GB/s 

RTX4000 

8GB GDDR6 

256-bit 

416 GB/s 

RTX5000 

16GB GDDR6 

256-bit 

448 GB/s 

The memory bandwidth you must deploy depends entirely on the workload type and the computational resources it requires. For example, if developing a large-scale ML project incorporating neural network layers, a wider memory bandwidth GPU will not let the project encounter bottlenecks. The more the memory bandwidth, the more efficiently the GPU cores undertake parallel processing.

Again, image and video-based ML projects such as image recognition and object identification demand more memory bandwidth than natural language processing (NLP) or sound processing workloads.

Enough memory bandwidth can accommodate a broad range of visual data in ML applications. Sound and text-based data are not heavy and hence can be handled on lower memory GPUs.

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