6 ms·

So nice to see GPU's being used for classical reasons again.

winwang 1y ago
Not sure if Spark is a classical portion for GPU compute ;) Well, outside of HPC and research. SQL on GPUs is definitely a research classic, dating back to 2004 at least: https://gamma.cs.unc.edu/DB/ https://gamma.cs.unc.edu/DB/
zackmorris 1y ago
Came here to say the same thing. Set Theory is the classical foundation of SQL: https://www.sqlshack.com/mathematics-sql-server-fast-introduction-set-theory/ https://www.sqlshack.com/mathematics-sql-server-fast-introdu... It's analogous to how functional programming expressed through languages like lisp is the classical foundation of spreadsheets. I believe that skipping first principles (sort of like premature optimization) is the root of all evil. Some other examples: - If TCP had been a layer above UDP instead of its own protocol beside it, we would have had real peer to peer networking this whole time instead of needing WebRTC. - If we had a common serial communication standard analogous to TCP for sockets, then we wouldn't need different serial ports like USB, Thunderbolt and HDMI. - If we hid the web browser's progress bar and used server-side rendering with forms, we could implement the rich interfaces of single-page applications with vastly reduced complexity by keeping the state, logic and validation in one place with no perceptible change for the average user. - If there was a common scripting language bundled into all operating systems, then we could publish native apps as scripts with substantially less code and not have to choose between web and mobile for example. - If we had highly multicore CPUs with hundreds or thousands of cores, then multiprocessing, 3D graphics and AI frameworks could be written as libraries running on them instead of requiring separate GPUs. And it's not just tech. The automative industry lacks standard chassis types and even OEM parts. We can't buy Stirling engines or Tesla turbines off the shelf. CIGS solar panels, E-ink displays, standardized removable batteries, thermal printers for ordinary paper, heck even "close enough" contact lenses, where are these products? We make a lot of excuses for why the economy is bad, but just look at how much time and effort we waste by having to use cookie cutter solutions instead of having access to the underlying parts and resources we need. I don't think that everyone is suddenly becoming neurodivergent from vaccines or some other scapegoat, I think it's just become so obvious that the whole world is broken and rigged to work us all to the grave to make some guy rich that it's giving all of us ADHD symptoms from having to cope with it.
- winwang 1y ago
  I'm not sure about the rest of your comment, but we would likely still want GPUs even with highly multicore CPUs. Case in point: the upper-range Threadripper series. It makes sense to have two specialized systems: a low-latency system, and a high-throughput system, as it's a real tradeoff. Most people/apps need low-latency. As for throughput and efficiency... turns out that shaving off lots of circuitry allows you to power less circuitry! GPUs have a lot of sharing going on and not a lot of "smarts". That doesn't even touch on their integrated throughput optimized DRAM (VRAM/HBM). So... not quite. We'd still be gaming on GPUs :)
  - zackmorris 1y ago
    Ya I winced after I wrote that and am considering taking a break from posting for a while. I'm just really tired of suffering the status quo because many people love it if it aligns with their worldview, while people like me who see things a little differently seem condemned to struggle in isolation. I've always admired Tesla, not Edison, but the Edison way of doing business seems to win out and suppress the people on the fringe. As for GPUs, they are useful for languages like GNU Octave (MATLAB) and Julia. Although there's a code smell with that. The matrix languages that would benefit most from SIMD seem to be the ones that aren't hardware accelerated on GPUs. Something has gone wrong: https://news.ycombinator.com/item?id=8302256 https://news.ycombinator.com/item?id=8302256 That was over 10 years ago and I doubt the situation is any better today. Misaligned incentives. I feel that the missing link is direct hardware access to the GPU ALUs. Without being able to transpile CPU code to GPU code and vice versa to provide bare metal multithreading and vector operations, we miss out on a whole branch of computer science. We can't do the interesting experiments that I always rant about, which would make stuff like genetic algorithms borderline trivial. We're stuck with cookie cutter solutions like OpenGL, Vulkan, Metal, etc. Yet we still praise Nvidia, even though like Microsoft/Intel in the 1990s, they're probably most responsibly for stifling innovation in multicore computing. For what it's worth, you're right about the latency/throughput tradeoff. I don't mind stuff like Intel's integrated graphics or Apple's M line of processors with integrated GPU and NPU cores for raw throughput and efficiency. I just think they're silly because they operate at the wrong layer: 1) CPU 2) multicore 3) GPU. There's no layer 2. Why is that?

Not sure if Spark is a classical portion for GPU compute ;) Well, outside of HPC and research. SQL on GPUs is definitely a research classic, dating back to 2004 at least: https://gamma.cs.unc.edu/DB/ https://gamma.cs.unc.edu/DB/

zackmorris 1y ago

Came here to say the same thing. Set Theory is the classical foundation of SQL: https://www.sqlshack.com/mathematics-sql-server-fast-introduction-set-theory/ https://www.sqlshack.com/mathematics-sql-server-fast-introdu... It's analogous to how functional programming expressed through languages like lisp is the classical foundation of spreadsheets. I believe that skipping first principles (sort of like premature optimization) is the root of all evil. Some other examples: - If TCP had been a layer above UDP instead of its own protocol beside it, we would have had real peer to peer networking this whole time instead of needing WebRTC. - If we had a common serial communication standard analogous to TCP for sockets, then we wouldn't need different serial ports like USB, Thunderbolt and HDMI. - If we hid the web browser's progress bar and used server-side rendering with forms, we could implement the rich interfaces of single-page applications with vastly reduced complexity by keeping the state, logic and validation in one place with no perceptible change for the average user. - If there was a common scripting language bundled into all operating systems, then we could publish native apps as scripts with substantially less code and not have to choose between web and mobile for example. - If we had highly multicore CPUs with hundreds or thousands of cores, then multiprocessing, 3D graphics and AI frameworks could be written as libraries running on them instead of requiring separate GPUs. And it's not just tech. The automative industry lacks standard chassis types and even OEM parts. We can't buy Stirling engines or Tesla turbines off the shelf. CIGS solar panels, E-ink displays, standardized removable batteries, thermal printers for ordinary paper, heck even "close enough" contact lenses, where are these products? We make a lot of excuses for why the economy is bad, but just look at how much time and effort we waste by having to use cookie cutter solutions instead of having access to the underlying parts and resources we need. I don't think that everyone is suddenly becoming neurodivergent from vaccines or some other scapegoat, I think it's just become so obvious that the whole world is broken and rigged to work us all to the grave to make some guy rich that it's giving all of us ADHD symptoms from having to cope with it.

winwang 1y ago
I'm not sure about the rest of your comment, but we would likely still want GPUs even with highly multicore CPUs. Case in point: the upper-range Threadripper series. It makes sense to have two specialized systems: a low-latency system, and a high-throughput system, as it's a real tradeoff. Most people/apps need low-latency. As for throughput and efficiency... turns out that shaving off lots of circuitry allows you to power less circuitry! GPUs have a lot of sharing going on and not a lot of "smarts". That doesn't even touch on their integrated throughput optimized DRAM (VRAM/HBM). So... not quite. We'd still be gaming on GPUs :)
- zackmorris 1y ago
  Ya I winced after I wrote that and am considering taking a break from posting for a while. I'm just really tired of suffering the status quo because many people love it if it aligns with their worldview, while people like me who see things a little differently seem condemned to struggle in isolation. I've always admired Tesla, not Edison, but the Edison way of doing business seems to win out and suppress the people on the fringe. As for GPUs, they are useful for languages like GNU Octave (MATLAB) and Julia. Although there's a code smell with that. The matrix languages that would benefit most from SIMD seem to be the ones that aren't hardware accelerated on GPUs. Something has gone wrong: https://news.ycombinator.com/item?id=8302256 https://news.ycombinator.com/item?id=8302256 That was over 10 years ago and I doubt the situation is any better today. Misaligned incentives. I feel that the missing link is direct hardware access to the GPU ALUs. Without being able to transpile CPU code to GPU code and vice versa to provide bare metal multithreading and vector operations, we miss out on a whole branch of computer science. We can't do the interesting experiments that I always rant about, which would make stuff like genetic algorithms borderline trivial. We're stuck with cookie cutter solutions like OpenGL, Vulkan, Metal, etc. Yet we still praise Nvidia, even though like Microsoft/Intel in the 1990s, they're probably most responsibly for stifling innovation in multicore computing. For what it's worth, you're right about the latency/throughput tradeoff. I don't mind stuff like Intel's integrated graphics or Apple's M line of processors with integrated GPU and NPU cores for raw throughput and efficiency. I just think they're silly because they operate at the wrong layer: 1) CPU 2) multicore 3) GPU. There's no layer 2. Why is that?