CPU and GPU differences

Question

What is the difference between a single processing unit of CPU and single processing unit of GPU?
Most places I\'ve come along on the internet cover the high level differen

Answer 1

In essence the major difference as far as writing code to run serially is clock speed of the cores. GPUs often have hundreds of fairly slow cores (Often modern GPUs have cores with speeds of 200-400 MHz) This makes them very bad at highly serial applications, but allows them to perform highly granulated and concurrent applications (such as rendering) with a great deal of efficiency.

A CPU however is designed to perform highly serial applications with little or no multi-threading. Modern CPUs often have 2-8 cores, with clock speeds in excess of 3-4 Ghz.

Often times highly optimized systems will take advantage of both resources to use GPUs for highly concurrent tasks, and CPUs for highly serial tasks.

There are several other differences such as the actual instruction sets, cache handling, etc, but those are out of scope for this question. (And even more off topic for SO)

Answer 2

Short answer

The main difference between GPUs and CPUs is that GPUs are designed to execute the same operation in parallel on many independent data elements, while CPUs are designed to execute a single stream of instructions as quickly as possible.

Detailed answer

Part of the question asks

In the CPU, the FPU runs real number operations. How fast are the same operations being done in each GPU core? If fast then why is it fast?

This refers to the floating point (FP) execution units that are used in CPUs and GPUs. The main difference is not how a single FP execution unit is implemented. Rather the difference is that a CPU core will only have a few FP execution units that operate on independent instructions, while a GPU will have hundreds of them that operate on independent data in parallel.

GPUs were originally developed to perform computations for graphics applications, and in these applications the same operation is performed repeatedly on millions of different data points (imagine applying an operation that looks at each pixel on your screen). By using SIMD or SIMT operations the GPU reduces the overhead of processing a single instruction, at the cost of requiring multiple instructions to operate in lock-step.

Later GPGPU programming became popular because there are many types of programming problems besides graphics that are suited to this model. The main characteristic is that the problem is data parallel, namely the same operations can be performed independently on many separate data elements.

In contrast to GPUs, CPUs are optimized to execute a single stream of instructions as quickly as possible. CPUs use pipelining, caching, branch prediction, out-of-order execution, etc. to achieve this goal. Most of the transistors and energy spent executing a single floating point instruction is spent in the overhead of managing that instructions flow through the pipeline, rather than in the FP execution unit. While a GPU and CPU's FP unit will likely differ somewhat, this is not the main difference between the two architectures. The main difference is in how the instruction stream is handled. CPUs also tend to have cache coherent memory between separate cores, while GPUs do not.

There are of course many variations in how specific CPUs and GPUs are implemented. But the high-level programming difference is that GPUs are optimized for data-parallel workloads, while CPUs cores are optimized for executing a single stream of instructions as quickly as possible.

Answer 3

Your question may open various answers and architecture design considerations. Trying to focus strictly to your question, you need to define more precisely what a "single processing unit" means.

On NVIDIA GPU, you have work arranged in warps which is not separable, that is a group of CUDA "cores" will all operate the same instruction on some data, potentially not doing this instruction - warp size is 32 entries. This notion of warp is very similar to the SIMD instructions of CPUs that have SSE (2 or 4 entries) or AVX (4 or 8 entries) capability. The AVX operations will also operate on a group of values, and different "lanes" of this vector unit may not do different operations at the same time.

CUDA is called SIMT as there is a bit more flexibility on CUDA "threads" than you have on AVX "lanes". However, it is similar conceptually. In essence, a notion of predicate will indicate whether the operations should be performed on some CUDA "core". AVX offers masked operations on its lane to offer similar behavior. Reading from and writing to memory is also different as GPU implement both gather and scatter where only AVX2 processors have gather and scatter is solely scheduled for AVX-512.

Considering a "single processing unit" with this analogy would mean a single CUDA "core", or a single AVX "lane" for example. In that case, the two are VERY similar. In practice both operate add, sub, mul, fma in a single cycle (throughput, latency may vary a lot though), in a manner compliant with IEEE norm, in 32bits or 64bits precision. Note that the number of double-precision CUDA "cores" will vary from gamer devices (a.k.a. GeForce) to Tesla solutions. Also, the frequency of each FPU type differs: discrete GPUs navigate in the 1GHz range where CPUs are more in the 2.x-3.xGHz range.

Finally, GPUs have a special function unit which is capable of computing a coarse approximation of some transcendental functions from standard math library. These functions, some of which are also implemented in AVX, LRBNi and AVX-512, perform much better than precise counterparts. The IEEE norm is not strict on most of the functions hence allowing different implementations, but this is more a compiler/linker topic.