Stream Processors vs CUDA Cores: What You Need to Know

Stream Processors vs CUDA Cores are two different ways to speed up your computer. Which one is best for you? Though both have their merits and shortcomings, it is important to know what each computer component has to offer before making a decision.

What is a Stream Processor and how does it work?

Stream Processors vs CUDA Cores

A stream processor is basically a computer chip. Stream processors are used for many different types of applications, from video games to image processing and machine learning.

Stream processors are very different from a CPU. A stream processor is designed to be used in parallel, so it can do many calculations at once.

Stream processing programs take advantage of this by assigning portions of the program or data (called "tasks") to each individual chip and then running them all simultaneously.

A stream processor has one main function that runs tasks: Processors Pipeline Stages Unit (PPS).

The PPS contains what we call Cuda Cores which are where instructions run and execute commands for streaming purposes only.

There's also an instruction cache that stores frequently accessed instructions close to the cores themselves as well as other caches such as shared memory buffers with high-speed access times for storing large amounts of temporary data from slower external memory.

How to calculate the number of Stream Processors in your GPU?

Stream Processors are the processors in a GPU that do not behave like CUDA Cores. Stream Processors perform general-purpose computations for graphics and other purposes, generally with lower latency than CUDA Cores.

A single shader instruction may execute simultaneously on multiple Stream Processors at the same time or different instructions can be executed sequentially on a single Stream Processor.

The number of stream processor (SP) units is not what you need to know when sizing your system requirements but if you want to find out how powerful your GPU is then divide SPs by two because each SP can handle only one operation per cycle while every CUDA core can handle up to four operations per cycle; this will give us an approximate idea about.

What are the Cuda Cores?

Stream Processors vs CUDA Cores

Cuda Cores refer to the number of processing units that can be used simultaneously in a GPU. The more CUDA cores available, the faster your application will perform and vice versa.

It’s important not only to know how many you need but also what type (integer or floating-point) since they each require different amounts of processors per operation (.05-.01 respectively), and rely on both parallelism and speed/efficiency with calculations as well.

One rule says if you're expecting 100k sequential instructions per second on average, then multiply this by .005-.01 which would give you 500-1000 for integer and 1000-1500 for floating-point respectively.

Why do you need to understand CUDA cores?

A CUDA core is a unit of processing on the graphics card. The more Cuda cores your GPU has, the faster it can render frames and process data in parallel.

It's important to note however that not all GPUs are created equal, some cards have higher amounts of CUDA cores than others.

If you're looking for an upgrade or want to know if your current GPU will be enough for what you need, check out our article "How many CUDA Cores should my Graphics Card have?"

  • Stream Processors: This acts as the brain of the GPU rendering engine by sending commands from memory with instructions about how to draw objects onscreen
  • Cuda Cores: These act as muscle cells within the CPU; they are the workhorses of a GPU.
  • Stream Processors: This acts as the brain of the GPU rendering engine by sending commands from memory with instructions about how to draw objects onscreen
  • Cuda Cores: These act as muscle cells within the CPU; they are the workhorses of a GPU.

What are the Cuda Threads?

A thread is a series of instructions that can be executed, so if you have multiple threads then your CPU will work more efficiently on different tasks simultaneously rather than waiting idly between each one.

The number of CUDA cores is not the same as Cuda Threads so if you have a high number of cores, then your CPU may be able to execute more CUDA threads at once than with a lower core count.

How many CUDA cores are in your GPU?

The size and number of the CUDA cores within your GPU are important factors for determining how powerful it is.

A GPU with a higher amount of CUDA Cores will be able to perform more computations in parallel than one that has less because on average they can process about ten times as many threads per core than CPUs.

The most recent Nvidia GPUs have up to 4432 CUDA cores.

How to calculate the number of CUDA cores you need for your application?

  • The number of CUDA cores that are integrated in a GPU, which is depended on the number of shader units and can vary from model to model.
  • The more CUDA cores available for use by applications running on the device, the faster they will run. However, not all calculations take advantage of parallelism; therefore it may be necessary to overcompensate when trying to maximize performance. A good rule of thumb is 100k sequential operations per second per core.
  • Given an expected processor count, there’s a relatively easy way to calculate the amount of floating-point operation each instruction performs (500 – 1000 operations). (.05-. 01 for integer operations).
  • There is no single best number of CUDA cores to pick!

The difference between the two processors - Stream Processors vs CUDA Cores

Stream processors are responsible for the handling of data from one stream to another. The more Cuda cores a GPU has, the easier it will be to process larger sets of information at once.

For example, if we want an image or video rendered then CUDA Cores can help speed up this rendering by dividing and processing different parts separately without waiting on the other part before moving onto the next step in order to finish sooner.

CUDA Cores are not as good at streaming tasks however they do excel in graphics applications such as gaming, computer-aided design programs, and simulations where there is less need for streams between input and output data (e.g., when rendering images).

Streams also tend to work better than CUDA Cores in applications such as video processing.

Streams are better for handling a continuous stream of input and output data, like when rendering images or videos.

It also excels at tasks that require more than one processor to process the same task simultaneously (e.g., transcoding), but not so much with graphics-related work because it is slower to render an image or simulate physics when compared to CUDA Cores.

When should I use one over the other - Streaming Processors or CUDA Cores?

You can use it when you need to process a large number of data points in parallel, or when you're dealing with huge datasets.

In our example below, we have two histograms - one computed using streaming processors and the other computed using CUDA Cores on graphics processing units (GPU).

The GPU-based histogram is substantially faster because it takes advantage of parallelism whereas this is not possible for streams.

This can be seen by comparing how long each took to compute: Stream 20 ms vs GPU 0.02 s.

Streams are great if you want efficient execution but don't care about speed; while GPUs may excel at high throughput tasks that require lots of floating-point calculations in hardware like deep learning networks, video encoding/ decoding, and scientific computing.

How many Stream Processors equals CUDA Cores?

It is a common misconception among many people that the number of Stream Processors on an Nvidia GPU equals CUDA Cores.

This is not true because, while they can be used interchangeably for certain tasks like data parallelism which shows up in AI applications and when building neural nets, it doesn't mean that there's a direct relationship between them.

This leads to confusion about how much computing power somebody has from one system versus another.

It also makes it hard to compare systems without knowing why someone chose one over the other option with different numbers of stream processors and Cuda cores.

If they're using both at the same time for tandem compute modes such as paralleling two GPUs together with double-precision convolutional network training.

Why developers should consider using OpenCL rather than CUDA?

  • Cuda Cores: Provides a way to harness the parallelism of modern hardware by dividing the work into smaller tasks. It can be used to execute these smaller tasks in parallel and is important for developers who are dealing with increasingly large datasets.
  • Stream vs Cuda Core Counts: For most applications that utilize stream processing it turns out that they need only one or two Cuda cores at any given time. When you have more than one core per application your usage will get divided up across those cores and so each individual task doesn't take as long because it's being performed on multiple threads simultaneously. This means using CUDA isn’t always necessary if all you want is high throughput streams (e.g., image processing with applications like Photoshop).
  • When you have more than two cores per application, Cuda is the way to go. It's well suited for tasks that need high compute capability in a tightly coupled parallel architecture (e.g., video encoding and some image processing) where it makes sense to divide up your work among multiple threads because of execution duration requirements or memory affinity constraints.
  • Why developers should consider using OpenCL rather than CUDA? The answer boils down to three major points: stream vs Cuda core counts, GPU hardware trends, and use case-specific needs. When deciding between these options there are several things worth considering-stream vs Cuda core counts, GPU hardware trends, and use case-specific needs.

Tips for choosing which one to use when designing your system

Stream - Data that can be processed in parallel on multiple cores. Streams are how the C programming language is programmed to use your computer's processors and memory, which is why most modern compilers for this type of program automatically generate streams instead of single-threaded code.

CAUDAs (Compute Adaptive Directives) - These directives provide an API to help you balance stream and device usage so that they perform more efficiently when running computations on GPUs versus CPUs or other devices where possible.

There are pros and cons for each method since all GPUs have different numbers of processors per core: Some APIs will run much faster than others depending on what kind of hardware you have.

When deciding which to use, it is important that the programmer understands what their system needs are and how they are going to generate output from input data.

A good rule of thumb when designing a program for GPUs is.

If your code executes on CPU or another device before transferring it onto GPU then CUDAs should be used instead of streams as Caudas allows more efficient memory management and performance in this situation.

Lowering latency by using Cuda Cores can also reduce power usage since there is no need for transfers between devices such as CPUs/GPUs if computations take place entirely on Cuda cores.

Frequently Asked Question

Question: I'm trying to decide which GPU I should buy and Cuda cores or stream processors are the deciding factors. What do you recommend?

Answer: Stream Processors, not CUDA Cores, is what matters for most graphics applications (games). This includes both VR content creation as well as CGI rendering in a game engine like Unity.

If your application primarily consists of tasks that involve parallel processing such as live video streaming from an IP camera feed, then having more CUDA Cores will have greater performance benefits than having more stream processors.

The two major considerations when choosing between these GPUs are whether the primary task requires many discrete steps that can be split up on a single CPU thread or if it needs to execute independently across multiple threads simultaneously.

Question: What's the difference between a stream processor and FPU?

Answer: A core is an execution unit. These are typically used for general operations, such as adding or multiplying two numbers together.

Stream processors are designed to work on streaming data that is unstructured and has many different sources (for example from network packets).

It can process large quantities of this type of information very quickly using pipelining techniques, but cannot add or multiply mathematically equivalent scalars like a CPU would be able to do with integers.

CUDA cores have both types of capabilities in them, so they're more versatile than stream processors which only focus on one thing."

Question: What are Cuda Cores?

Answer: CUDA cores compute the function for each element in this stream. The more processing power or "cores" you have, the faster your simulation will run on your hardware.

Question: How does streaming work?

Answer: Streaming refers to doing small amounts of computation as new pieces of information come in rather than waiting until all pieces arrive at once before starting processing them.

There's no need to wait for all parts of a transaction to arrive before they're processed - just collect those that get there quickly enough and then process them one by one (stream).

This technique works well with video encoding because the video is broken into a series of frames.


In this post, we’ve explored the benefits and drawbacks of stream processors versus CUDA Cores. It is important to consider both types of hardware when choosing what kind of neural network processor you need for your deep learning applications. While stream processors are cheaper than their CUDA counterparts, they also do not provide many floating-point operations per second (FLOPS) – which can slow down processes that require higher computational power such as training a model or processing lots of data.

- For example: Let's say you want to train an AI algorithm using GPU acceleration on 50 GPUs with 128 GB RAM each one at time step t in order to meet deadlines from customers requesting new features every day - then go ahead and use Stream Processors.

- On the other hand, if you want to train a deep neural network with CUDA Cores and go through billions of data points in order to reach more accurate predictions – then use that instead.

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