What is FLOPS in field of deep learning?

Question

What is FLOPS in field of deep learning? Why we don't use the term just FLO?

We use the term FLOPS to measure the number of operations of a frozen deep learning network.

Following Wikipedia, FLOPS = floating point operations per second. When we test computing units, we should consider of the time. But in case of measuring deep learning network, how can I understand this concept of time? Shouldn't we use the term just FLO(floating point operations)?

Why do people use the term FLOPS? If there is anything I don't know, what is it?

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Frozen deep learning networks that I mentioned is just a kind of software. It's not about hardware. In the field of deep learning, people use the term FLOPS to measure how many operations are needed to run the network model. In this case, in my opinion, we should use the term FLO. I thought people confused about the term FLOPS and I want to know if others think the same or if I'm wrong.

Please look at these cases:

how to calculate a net's FLOPs in CNN

https://iq.opengenus.org/floating-point-operations-per-second-flops-of-machine-learning-models/

Direful

Answer 1

I not sure my answer is 100% correct. but this is what i understand.

• FLOPS = Floating point operations per second

• FLOPs = Floating point operations

FLOPS is a unit of speed. FLOPs is a unit of amount.

Pigling

Thank you for your answer. I think you answered correctly, but I can't understand why people press down button. – Direful

So the higher FLOPS is the better(quicker) and the lower the FLOPs is the better(less operations). right? – Bulley

@Bulley Yes indeed. – Pithos

Answer 2

Confusingly both FLOPs, floating point operations, and FLOPS, floating point operations per second, are used in reference to machine learning. FLOPs are often used to describe how many operations are required to run a single instance of a given model, like VGG19. This is the usage of FLOPs in both of the links you posted, though unfortunately the opengenus link incorrectly mistakenly uses 'Floating point operations per second' to refer to FLOPs.

You will see FLOPS used to describe the computing power of given hardware like GPUs which is useful when thinking about how powerful a given piece of hardware is, or conversely, how long it may take to train a model on that hardware.

Sometimes people write FLOPS when they mean FLOPs. It is usually clear from the context which one they mean.

Internship

It is too late, but I want to say thank you for your kind answer. – Direful

Answer 3

What is FLOPS in field of deep learning? Why we don't use the term just FLO?

FLOPS (Floating Point Operations Per Second) is the same in most fields - its the (theoretical) maximum number of floating point operations that the hardware might (if you're extremely lucky) be capable of.

We don't use FLO because FLO would always be infinity (given an infinite amount of time hardware is capable of doing an infinite amount of floating point operations).

Note that one "floating point operation" is one multiplication, one division, one addition, ... Typically (for modern CPUs) FLOPS is calculated from repeated use of a "fused multiply then add" instruction, so that one instruction counts as 2 floating point operations. When combined with SIMD a single instruction (doing 8 "multiple and add" in parallel) might count as 16 floating point instructions. Of course this is a calculated theoretical value, so you ignore things like memory accesses, branches, IRQs, etc. This is why "theoretical FLOPs" is almost never achievable in practice.

Why do people use the term FLOPS? If there is anything I don't know, what is it?

Primarily it's used to describe how powerful hardware is for marketing purposes (e.g. "Our new CPU is capable of 5 GFLOPS!").

Spinach

Thank you very much for your answer. Sorry I couldn't fully understand what you said about instructions, because of my poor background of the field of hardware, but I agree what you are going to explain about the term of FLOPS. But.... you may miss my question, so I added some sentences to clear it. – Direful

Thank you very much. Why some models have higher FLOPs than others? Is it because we run on a better GPU only? – Runyan

@Avv: In general, you'd increase "theoretical max. FLOPS" by being able to do more floating operations in parallel (e.g. wider SIMD doing 16 operations in parallel instead of 8 operations in parallel), or by doing each floating point operation faster (e.g. higher clock rate). Of course "higher theoretical max. FLOPS" can be "slower performance in practice" and can easily be "worse performance per watt". Ironically; if you design a large supercomputer around "How much performance can we get out of 1234 MW" you end up with a huge number of slow/efficient nodes.. – Spinach

Answer 4

In the field of deep learning, FLOPS stands for Floating Point Operations Per Second. It is a measure of a computer's performance, specifically how many floating-point calculations it can perform in one second. Here’s a simple breakdown:

What are Floating Point Operations?

Floating point operations include basic arithmetic operations like addition, subtraction, multiplication, and division that involve decimal numbers (floating-point numbers).

What Does FLOPS Measure?

FLOPS measures the computational power of hardware, such as CPUs or GPUs. It tells us how many floating-point operations the hardware can perform every second. For example, a GPU with a performance of 5 TFLOPS (teraflops) can perform 5 trillion floating-point operations per second.

Why is FLOPS Important in Deep Learning?

Model Complexity: In deep learning, models often require a large number of floating-point operations to process data and make predictions. FLOPS helps quantify the computational complexity of these models.

Performance Benchmarking: It allows us to compare the performance of different hardware setups. Higher FLOPS generally means faster computation, which is crucial for training and running deep learning models efficiently.

Resource Allocation: Knowing the FLOPS of a model and the hardware helps in planning and allocating the right resources for training and inference tasks.

Example Consider a deep learning model that processes images. The number of FLOPS required to run this model gives an idea of how computationally intensive it is. If a model requires 1 billion FLOPS to process one image, and your GPU can handle 5 billion FLOPS per second, it means your GPU can process 5 images per second.

FLOPS vs. FLOP FLOP (Floating Point Operation) refers to a single floating-point calculation. FLOPS (Floating Point Operations Per Second) measures how many such operations can be performed in one second.

Practical Use

When optimizing a deep learning model, you might aim to reduce the number of FLOPS required to make it run faster on available hardware. Conversely, when choosing hardware, you might look for higher FLOPS to ensure it can handle complex models efficiently.

In summary, FLOPS is a key metric in deep learning that helps gauge the computational power needed to train and run models, ensuring efficient and effective use of hardware resources.

Gramicidin

This answer looks like a copy-paste from ChatGPT. Which presumably tells us something about the quality of the website that your profile appears to be promoting. – Fylfot