What Is Memory Bandwidth GPU? (Explained)

This blog examines memory bandwidth, one of the GPU features that is frequently disregarded. We’ll delve into the concept of What Is Memory Bandwidth GPU? and discuss why it needs to be taken into account as one of the features an ML professional should search for in a machine learning platform. An essential step in creating a model is comprehending the memory requirements for machine learning. Nevertheless, it can be simple to forget about.

What Is Memory Bandwidth GPU?

GPU memory bandwidth is a measurement of the speed at which data is sent from a GPU to the system across a bus like Thunderbolt or PCI Express. When creating your high-performance Metal programs, it’s crucial to consider the bandwidth of each GPU in the system.

The Basic GPU Anatomy

A graphics card’s central processing unit, memory, and power management module are all on a printed circuit board. The card also features a BIOS chip that handles startup diagnostics on the memory, input, and output while preserving the card’s settings.

Similar to the CPU on a computer’s motherboard, the graphics processing unit (GPU) on a graphics card performs graphics-related tasks. Contrarily, a GPU is made to complete the intricate mathematical and geometric computations necessary for producing graphics or other machine learning-related applications.

The memory interface Bus connects a graphics card’s compute unit (GPU) to its memory unit (VRAM, or video random access memory). There are several memory interfaces present in a computer system. The physical bit-width of the memory bus about the GPU is known as a memory interface.

Every clock cycle, data is transferred to and from the on-card memory (billions of times per second). The width of this interface typically referred to as “384-bit” or something similar, is the physical number of bits that may fit along the bus every clock cycle.

Every clock cycle, 384 bits of data can be exchanged via a 384-bit memory interface. As a result, the memory interface significantly determines the maximum memory throughput on a GPU. As a result, standardized serial point-to-point buses are more likely to be used by NVIDIA and AMD in their graphics cards.

The POD125 standard, which effectively outlines the communication protocol with GDDR6 VRAM, is utilized by the A4000, A5000, and A6000 NVIDIA Ampere series graphics cards that are accessible to Paperspace users.

Latency is a second aspect to take into account when discussing memory bandwidth. The VMEbus and S-100 bus were used in the beginning. However, modern memory buses are made to link directly to VRAM chips to reduce latency.

GDDR5 and GDDR6 memories, two of the most recent GPU memory standards, offer an example. Each memory consists of two chips, each of which has a 32-bit bus (two parallel 16-bits) that enables numerous concurrent memory accesses. With eight GDDR6 memory chips, a GPU with a 256-bit memory interface will be equipped.

HBM and HBM2 (high bandwidth memory v1 and v2) are two alternative memory standards; with these standards, each HBM interface is 1024 bits, often enabling better bandwidths than GDDR5 and GDDR6.

This internal memory interface should not be mistaken for the external PCI-Expression link between the motherboard and the graphics card. Although it is a lot slower, this bus is distinguished by its bandwidth and speed.

What Is GPU Memory Bandwidth?

How quickly data may be transferred from/to memory (vRAM), and the computing cores depends on the GPU’s memory bandwidth. The indicator is more accurate than GPU Memory Speed. It depends on the bus’s number of parallel links and the data transfer speed between the memory and computed cores.

Since the early 1980s, home computers (1MB/s) and unlimited memory bandwidths in consumer electronics have increased by several orders, but compute resources have grown even faster. The only way to prevent constantly hitting bandwidth limits is to ensure that workloads and resources have the same order of magnitude in terms of memory size and bandwidth. Let’s look at the NVIDIA RTX A4000, one of the most cutting-edge GPUs for machine learning, as an example:

It has a 256-bit memory interface (number of individual links on the bus between the GPU and VRAM), 16 GB of GDDR6 memory, and an astounding 6144 CUDA Cores. The A4000 can achieve a memory bandwidth of 448 GB/s with all these memory-related features.

Why Do Machine Learning Applications Require High Memory Bandwidth?

Memory bandwidth has an influence that is not immediately apparent. All those thousands of GPU computing cores will be idle while they wait for a memory response if it’s too sluggish, causing the system to bottleneck. Additionally, depending on the application, if the GPU can process data blocks repeatedly (let’s say T times), then the external PCI bandwidth must be 1/Tth of the GPU’s internal bandwidth.

The most typical use of a GPU illustrates the restriction above. For instance, a model training application might load training data into GDDR RAM and run multiple neural network layer computations on the compute cores for hours at a time. Therefore, the PCI bus bandwidth to GPU internal bandwidth ratio may range from 20 to 1.

The kind of project you’re working on will determine just how much memory bandwidth you need. For instance, you will require a wider memory bandwidth if you are working on a deep learning project that depends on vast amounts of data being supplied, processed again, and continuously restored in memory.

The memory and memory bandwidth requirements for a video and image-based machine learning project are higher than for a natural language or sound processing project. A suitable range for most ordinary projects is between 300 and 500 GB/s. Although it isn’t always the case, there is typically enough memory bandwidth to support various machine learning applications for visual data.

Let’s examine an illustration of the validation of deep learning memory bandwidth requirements: If we utilize a 32-bit floating point to store a single parameter from the 50-Layer ResNet, which has over 25 million weight parameters, it would require over 0.8GB of memory.

Therefore, each model pass during parallel computing with, for instance, a mini-batch of size 32 would require 25.6GB of memory to be loaded. Given that the ResNet model requires 497 GFLOPs in a single pass (for the case with a feature size of 7 x 7 x 2048) and that a GPU like the A100 is capable of 19.5 TFLOPs.

And we would be able to complete about 39 full passes per second, resulting in a bandwidth requirement of 998 GB/s. Therefore, the A100, with its 1555 GB/s of bandwidth, would be capable of handling this model effectively and avoiding bottlenecking.

How Can Models Be Made To Use Less Memory Bandwidth?

In general, deep neural networks in computer vision, in particular, and machine learning methods cause a significant memory and memory bandwidth footprint. Several ways can be utilized to deploy ML models in environments with limited resources, including vital cloud ML services to save money and time. The following are some of the tactics that can be used:

A partial fit if more than one pass is required to fit the dataset. With the help of this capability, you can do a model to the data incrementally rather than all at once. Thus, a piece of information is taken, fitted to produce a weight vector, and then the process moves on to the next part of data, worked to make another weight vector, and so on.

This reduces VRAM use while lengthening training. The biggest problem is that not all implementations and algorithms use partial fit or can technically be modified to do so. However, it should be considered whenever possible.

Dimensionality reduction: This is crucial for cutting back on time spent training and the amount of memory used while running. Several methods, including Principal component analysis (PCA), Linear discriminant analysis (LDA), and matrix factorization, can significantly reduce dimensionality and produce subsets of the input variables with fewer features while preserving some of the significant characteristics of the original data.

A dense matrix storing only the non-zero entries of a sparse matrix can result in significant memory savings. In comparison to the fundamental technique, several data structures can be used depending on the quantity and distribution of non-zero objects, which results in significant memory savings.

In exchange for decreased memory bandwidth use, the trade-off is that accessing individual components becomes more challenging, and additional structures are needed to recover the original matrix without ambiguity.

Final Verdict

Model design involves understanding the memory bandwidth requirements for machine learning. Reading this post has provided you with knowledge on memory bandwidth after assessing the applicability and the evaluation of memory bandwidth needs. We spoke about What Is Memory Bandwidth GPU? and several ways to reduce prices and bandwidth usage while maintaining timing and accuracy standards by choosing a less capable cloud package.

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