That's orthogonal to SIMD data parallelism. You want to write code that can take advantage of both, e.g. to execute vector FMA instructions at 2 per clock cycle, with each instruction doing 8 float FMAs, for a total throughput of 16 float FMA ops per clock. Data parallelism can be exposed to a CPU via SIMD x ILP x threads. –

2015年5月17日 · SIMD is the 'concept', SSE/AVX are implementations of the concept. All SIMD instruction sets are just that, a set of instructions that the CPU can execute on multiple data points. As long as the CPU supports executing the instructions, then it is feasible for multiple SIMD instruction sets to coexist, regardless of data size.

2019年3月18日 · But anyway, SIMD is not specific to GPUs at all. Most high-performance CPU architectures have SIMD extensions too, like x86 SSE/SSE2 that allows one instruction to work with 128-bit registers as 4x float , 2x double , or any of 16x 8-bit integer, or 16/32/64-bit integer.

2009年9月14日 · Modern CPUs provide direct support for vector operations where a single instruction is applied to multiple data (SIMD). For example, a CPU with a 512 bit register could hold 16 32- bit single precision doubles and do a single calculation. 16 times faster than executing a single instruction at a time.

2014年9月2日 · Or consider the memcmp example: all that needs to be "unpacked" is a single summary bit of the register. Of course the branch itself is not a SIMD instruction, but that's because it doesn't have to be: SIMD can easily offload it to the CPU's branch machinery. GPUs don't have that luxury. –

2015年2月16日 · Normally throughput is the number of instructions per clock cycle, but this is actually reciprocal throughput: the number of clock cycles per independent instruction start - so 0.5 clock cycles means that 2 instructions can be issued in one clock cycle and the result is ready on the next clock cycle.

2023年11月8日 · CPU SIMD is a close equivalent to what you want. I think really the CPU equivalent of this GPU architecture is CPU-style SIMD using short fixed-width vectors (like 256-bit vectors of 8x 32-bit elements or 32x 8-bit elements, or whatever, depending on the instruction).

The second pass you can also use SIMD since a constant value is being added to each partial sum. Assuming n elements of an array, m cores, and a SIMD width of w the time cost should be. n/m + n/(m*w) = (n/m)*(1+1/w) Since the fist pass does not use SIMD the time cost will always be greater than n/m

The equivalent of a CPU core on a GPU is a "symmetric multiprocessor": It has its own instruction scheduler/dispatcher, its own L1 cache, its own shared memory etc. It is CUDA thread blocks rather than warps that are assigned to a GPU core, i.e. to a streaming multiprocessor.

2019年6月1日 · IDK if it's a good idea to mention Linux kernel modules using SIMD without warning that you need kernel_fpu_begin() / _end() around your SIMD code. An LKM is the last place you would expect to find SIMD, and the hardest place to test it, so it seems maybe confusing to bring that up as the first steps in an intro-to-SIMD answer. –