41–50 of 127 posts
Looks like a great use case for AI. Set up the logical specification and constraints and let the AI find the optimal sequence of SIMD operations to fulfill the requirements.
I see a lot of "just use the GPU" and you'd often be right. SIMD on the CPU is most compelling to me due to the latency characteristics. You are nanoseconds away from the control flow. If the GPU needs some updated state regarding the outside world, it takes significantly longer to propagate this information. For most use cases, the GPU will win the trade off. But, there is a reason you don't hear much about systems…
You would be surprised. The GPU often loses even for small neural nets given the large latency. Anything that needs high throughput or is sized like an HPC problem should use a GPU, but a lot of code benefits from SIMD on small problems.
The one truly unfixable issue is round-trip latency.
Looks like a great use case for AI. Set up the logical specification and constraints and let the AI find the optimal sequence of SIMD operations to fulfill the requirements.
lol so says every person that has no clue how (NP-hard) combinatorial optimization is.
I see a lot of "just use the GPU" and you'd often be right. SIMD on the CPU is most compelling to me due to the latency characteristics. You are nanoseconds away from the control flow. If the GPU needs some updated state regarding the outside world, it takes significantly longer to propagate this information. For most use cases, the GPU will win the trade off. But, there is a reason you don't hear much about systems…
Do Apple's chips (M1 etc) change this at all, since they share memory with the GPU?
I see a lot of "just use the GPU" and you'd often be right. SIMD on the CPU is most compelling to me due to the latency characteristics. You are nanoseconds away from the control flow. If the GPU needs some updated state regarding the outside world, it takes significantly longer to propagate this information. For most use cases, the GPU will win the trade off. But, there is a reason you don't hear much about systems…
Do Apple's chips (M1 etc) change this at all, since they share memory with the GPU?
Earlier quoted context omitted.
lol so says every person that has no clue how (NP-hard) combinatorial optimization is.
For humans it's very hard but it will be a breeze for the AI. I thought HN was a community of builders. This is an obvious startup opportunity.
Earlier quoted context omitted.
For humans it's very hard but it will be a breeze for the AI. I thought HN was a community of builders. This is an obvious startup opportunity.
All we have to do is ascribe magical properties to AI and we can solve anything as if P=NP!
The main problem is that there are no good abstractions in popular programming languages to take advantage of SIMD extensions. Also, the feature set being all over the place (e.g. integer support is fairly recent) doesn't help either. ISPC is a good idea, but execution is meh... it's hard to setup and integrate. Ideally you would want to be able to easily use this from other popular languages, like Java, Python, Java…
Earlier quoted context omitted.
I wish hardware exposed an api that allowed us to submit a tree of instructions so the hardware doesn’t need figure out which instructions are independent. Lots of this kind of work can be done during compilation but cannot be communicated to hardware due to code being linear
That's called VLIW and Intel Itanium is considered one of the biggest chip failures of all time. There is an argument that today's compilers are finally good enough for VLIW to go mainstream, but good luck convincing anyone in today's market to go for it. ------ A big problem with VLIW is that it's impossible to predict L1, L2, L3 or DRAM access. Meaning all loads/stores are impossible to schedule by the compiler. NV…
The idea pyrolistical mentioned is closer to explicit data graph execution: https://en.m.wikipedia.org/wiki/Explicit_data_graph_executio....
I see a lot of "just use the GPU" and you'd often be right. SIMD on the CPU is most compelling to me due to the latency characteristics. You are nanoseconds away from the control flow. If the GPU needs some updated state regarding the outside world, it takes significantly longer to propagate this information. For most use cases, the GPU will win the trade off. But, there is a reason you don't hear much about systems…
You would be surprised. The GPU often loses even for small neural nets given the large latency. Anything that needs high throughput or is sized like an HPC problem should use a GPU, but a lot of code benefits from SIMD on small problems.
Apple's neural engine shows that you can live in between those two worlds.
As you said, the trouble is the latency, the programming model is still great.