VectorGL

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VectorGL

VectorGL

@Vector_GL

Graphics and design professional. Creator of Rasterizer.

macOS Katılım Mayıs 2014
55 Takip Edilen1.1K Takipçiler
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Eric Lengyel
Eric Lengyel@EricLengyel·
New blog post: A Decade of Slug This talks about the evolution of the Slug font rendering algorithm, and it includes an exciting announcement: The patent has been dedicated to the public domain. terathon.com/blog/decade-sl…
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Abhishek
Abhishek@shekitup·
Sim #55 - Does this angle really never change? hard to believe till you see it but the angle between two chords stays the same at every point on the arc. and it's always half the central angle.
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Rive
Rive@rive_app·
Curious why Scripting runs on Luau? Our CTO @luigirosso breaks down the architecture, tradeoffs, and why a fast, safe, lightweight language makes the most sense for real-time graphics: rive.app/blog/why-scrip…
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inigo quilez
inigo quilez@iquilezles·
Here's some cute bounding box functions for 2D shapes. A link to a longer list in the next post.
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VectorGL
VectorGL@Vector_GL·
Rasterizer, the 3rd iteration of my 2D GPU-accelerated vector graphics engine, is now available. • Around 30x faster than Core Graphics • Less than 4,000 lines of code • CPU stages use efficient batch parallelism • Quadratic Béziers solved on GPU github.com/mindbrix/Raste…
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OutsideOfSociety
OutsideOfSociety@oosmoxiecode·
Open for new job or freelancing in the near future. Anything with realtime graphics, prototyping, ui/ux, rnd, vfx, games, creative coding, etc. Feel free to reach out, DMs should be open. #UnrealEngine #Unity #threejs #godot
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Vivek Galatage
Vivek Galatage@vivekgalatage·
This has to be one of the best GPU programming resources I've found - the GPU Glossary from Modal breaks down complex concepts with clear visuals and explanations, from CUDA architecture to Tensor Cores to CTAs. modal.com/gpu-glossary
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Vivek Galatage@vivekgalatage

Understanding GPU Architecture from Cornell cvw.cac.cornell.edu/gpu-architectu… During a low-level discussion at a casual meetup, many folks were interested in understanding GPUs more closely. While CPUs optimize for complex control flow (see those big cores + caches), the GPUs maximize throughput with thousands of simple cores sharing memory. The GPU architecture roadmap is a good starting point for diving deeper.

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Grant Sanderson
Grant Sanderson@3blue1brown·
There is a lot about Euclid’s Elements that is easily misunderstood. Some proofs seem to have logical gaps. Some constructions seem pointless, others seem needlessly convoluted. Each of these provides a window into how the ancient Greeks thought about math and the philosophical role that geometry played. In the fifth and final of a series of guest videos I've been posting, @BenSyversen delves into a question anybody who has had to do ruler and compass constructions in a geometry class may have wondered: What's the point? youtu.be/M-MgQC6z3VU
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Sebastian Aaltonen
Sebastian Aaltonen@SebAaltonen·
Flutter (Impeller) uses one command buffer for one render pass. Yes, a single frame can use 30 command buffers :)
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Zephyr
Zephyr@zephyr_z9·
> be me, NVIDIA in 2006 > making GPUs so gamers can play World of Warcraft in HD > Jensen Huang has a vision > "what if we made them do... math?" > entire boardroom thinks he's finally lost it > create CUDA > a software prison so elegant, academics will volunteer to be inmates > gamers are confused. "Will this make Crysis run faster?" > Jensen: "...no :)" > fast forward to 2012 > AI researchers, hopped up on adderall and free pizza, discover they need a shitload of matrix multiplication to make cat pictures > their Intel Xeons are crying, smoking, and filing for divorce > they discover CUDA > "holy shit this math thing you made is perfect" > the entire field of AI becomes a subsidiary of a company known for making Fortnite run better > we see this and our eyes turn into dollar signs > decide the prison needs better amenities > invent Tensor Cores > "what if part of the chip did ONLY the AI math, but did it at ludicrous speed?" > it's like adding a nitro booster to a Honda Civic specifically for going to the grocery store > AMD's competing card is over there trying to do the same math with a abacus and hope > because researchers are so lazy they can't be trusted to write a for-loop > just give them a big red "MAKE AI" button that runs our code > create cuDNN > bake it directly into TensorFlow and PyTorch > ecosystem lock-in is complete. We own the land, the factory, and the souls of the workers. > a bunch of hippies from a thing called "OpenAI" call > they want to build AGI or something > they need compute > roll up to their office, which is probably a converted garage, with the world's first DGX-1 > it's like the monolith from Space Odyssey > I tell them it has 8 GPUs connected by NVLink, so they can gossip like schoolgirls at light speed > they ask what it does > "It does AI, idiots. Just plug it in." > they sign the papers. > But we're not done. The prison needs a better yard. > See them trying to connect our beautiful DGX boxes with CAT-5 cables > embarrassing. > Acquire Mellanox for their InfiniBand tech > Now the nervous system connecting the GPUs is also ours > NVSwitch makes an entire rack of GPUs hold hands so tight they become one colossal mega-brain > Meanwhile, in a dimly lit basement in Santa Clara... AMD > They drop a new GPU. "The AMD Instinct MI300X! We have more theoretical FLOPs!" > "Better Benchmark performance." > One brave researcher tries to use it. > spends 6 weeks trying to install ROCm, their knock-off wish.com version of CUDA > gets 400 errors, a kernel panic, and divorce papers from his wife > he finally gets a model to run at half the promised speed > The benchmark was for a matrix size of 1x1 > He throws the card in the trash and buys an NVIDIA H100 on the company card. The cost is irrelevant. > Sanity is priceless. > Jensen takes the stage. The leather jacket has its own gravitational pull. Unleashes the GB200 NVL72 > 72 GPUs and 36 Grace CPUs in a single NVLink domain with 130 TB/s of bandwidth > the GPUs share thoughts before they even have them. > 2025 > AMD comes out with their "rack scale" solution, the MI355X 128-GPU rack > It's just 16 groups of 8 GPUs duct-taped together with Ethernet > A scale out clown car > We announce Rubin CPX GPU enabling cheaper prefill during inference > saving $$$$ > it's the ultimate vendor lock-in > every other chip designer sees this and has to throw their architecture in the trash and go back to the drawing board > All hail the leather-clad prophet.
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VectorGL
VectorGL@Vector_GL·
@AgileJebrim @SebAaltonen “Best practice” is an industry hustle ;-) Start simple & flat. Group and abstract only when necessary. Bottom up.
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Jebrim
Jebrim@AgileJebrim·
@Vector_GL @SebAaltonen Ya always a problem with highly abstracted codebases. I would love to see everything inlined into one big function. Unfortunately that’s considered an anti-pattern by most of the industry.
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Jebrim
Jebrim@AgileJebrim·
@Vector_GL @SebAaltonen That’s why you use an API trace in a GPU debugger to see what is actually happening in what order.
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Kari Lavikka |
Kari Lavikka |@KariLavikka·
Here's a clip from the lightning talk I'm thrilled to present in Friday's poster session at #VIZBI 2021! GenomeSpy is a #WebGL-accelerated visualization tool with a @vega_vis-inspired visualization grammar. #biovis #dataviz (1/4)
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VectorGL
VectorGL@Vector_GL·
@CMichael85937 Good question. I don’t know Vello very well, but it did have performance issues at certain view scales which Rasterizer does not. as it is designed to handle very large scenes gracefully. If you can run both locally, compare them rendering TestFiles/pathological.pdf.
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VectorGL
VectorGL@Vector_GL·
@cifilter No. Path interpolation was considered outside the very narrow scope of the project. Path creation is pretty efficient, so this could be done in user space.
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Shannon Potter
Shannon Potter@cifilter·
@Vector_GL Does it have better path interpolation support than Core Animation for animating paths? Managing specific control points that linearly interpolate in a nice way from path A to B has always been a massive pain.
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