Accelerate product breakthroughs with Rescale's digital engineering platform that transforms HPC, modeling and simulation data into engineering e
Users generally appreciate Rescale for its robust cloud-based high-performance computing capabilities, which allow for efficient scaling and management of complex computational tasks. However, a few users comment on its complex setup process and occasionally steep learning curve. Pricing sentiment is mixed, with some users feeling it's on the higher side compared to alternatives but acknowledging the value it brings for large-scale computing needs. Overall, Rescale is regarded as a powerful and effective solution for businesses requiring substantial computational power, albeit with some room for user experience improvements.
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Users generally appreciate Rescale for its robust cloud-based high-performance computing capabilities, which allow for efficient scaling and management of complex computational tasks. However, a few users comment on its complex setup process and occasionally steep learning curve. Pricing sentiment is mixed, with some users feeling it's on the higher side compared to alternatives but acknowledging the value it brings for large-scale computing needs. Overall, Rescale is regarded as a powerful and effective solution for businesses requiring substantial computational power, albeit with some room for user experience improvements.
Features
Use Cases
Industry
information technology & services
Employees
240
Funding Stage
Series D
Total Funding
$284.9M
gpt-5.5 API is randomly and inconsistently resizing image inputs
I'm asking the gpt-5.5 API to identify (x, y) coordinates of particular features in an input image (a JPEG). The good news is that gpt-5.5 does much, much better at this task than gpt-5.4 did. It's like night and day. The confusing thing is that gpt-5.5 randomly resizes the image, which makes it hard to interpret the (x, y) coordinates. I've included this instruction in my prompt: Do not resize, pad or crop the image. Give (x, y) coordinates for the input image. As part of the output, I've asked it to include the height and width of the image. I can run the exact same prompt multiple times it gives back different heights/widths. Sometimes they're the same as the input image. Sometimes it's made the image larger with the same aspect ratio. Sometimes it's scaled it in a way that alters the aspect ratio. The input image is 1024x1514px, but I've seen similar behavior with different sizes. I'm uploading it base64-encoded with "detail": "original". gpt-5.4 did not do this. Has anyone else seen this? I can rescale the coordinates, but I feel nervous that I have no idea why this is happening. submitted by /u/danvk [link] [comments]
View originalFlashAttention (FA1–FA4) in PyTorch - educational implementations focused on algorithmic differences [P]
I recently updated my FlashAttention-PyTorch repo so it now includes educational implementations of FA1, FA2, FA3, and FA4 in plain PyTorch. The main goal is to make the progression across versions easier to understand from code. This is not meant to be an optimized kernel repo, and it is not a hardware-faithful recreation of the official implementations. The point is to expose the algorithmic ideas and design changes without immediately going deep into CUDA/Hopper/Blackwell-specific details. Roughly, the repo now shows: FA1: tiled online softmax baseline FA2: split-Q / query-tile ownership, deferred normalization FA3: explicit staged pipeline with ping-pong tile buffers, plus a simplified educational FP8 forward path FA4: explicit scheduler with main / softmax / correction phases, and conditional/selective rescaling So the same exact attention math is preserved, but the orchestration changes version by version. I wrote it for people who want to understand: "What actually changed from FA1 → FA2 → FA3 → FA4?"" without having to start from highly optimized CUDA kernels. Repo: https://github.com/shreyansh26/FlashAttention-PyTorch Would be interested in feedback on whether the code makes the version-to-version differences intuitive. submitted by /u/shreyansh26 [link] [comments]
View originalRescale uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Optimize, Accelerate, Transform, Expand, Rescale High Performance Computing, Advanced Modeling And Simulation, Agentic Digital Engineering, AI Physics.
Rescale is commonly used for: HPC Cloud Bursting Migration, Explore the Future of Digital Engineering.
Rescale integrates with: ANSYS, COMSOL Multiphysics, MATLAB, Siemens NX, Autodesk, OpenFOAM, SolidWorks, Altair HyperWorks, LS-DYNA, Fluent.
