Juleps
glow
| Juleps | glow | |
|---|---|---|
| 2 | 6 | |
| 67 | 3,164 | |
| - | 1.4% | |
| 0.0 | 8.2 | |
| almost 4 years ago | 4 days ago | |
| C++ | ||
| GNU General Public License v3.0 or later | Apache License 2.0 |
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Juleps
glow
-
Accelerating AI inference?
Pytorch supports other kinds of accelerators (e.g. FPGA, and https://github.com/pytorch/glow), but unless you want to become a ML systems engineer and have money and time to throw away, or a business case to fund it, it is not worth it. In general, both pytorch and tensorflow have hardware abstractions that will compile down to device code. (XLA, https://github.com/pytorch/xla, https://github.com/pytorch/glow). TPUs and GPUs have very different strengths; so getting top performance requires a lot of manual optimizations. Considering the the cost of training LLM, it is time well spent.
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Decompiling x86 Deep Neural Network Executables
It's pretty clear its referring to the output of Apache TVM and Meta's Glow
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US government bans export of NVIDIA A100 to China and Russia, effective immediately
I also disagree with this. For example, Meta seems desperate about AI accelerators, and in fact is already doing "hardware customers develop software stack themselves" I mentioned above: Glow is that stack. Meta is doing Glow even if there is no promising AI accelerators right now, they are that desperate.
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If data science uses a lot of computational power, then why is python the most used programming language?
For reference: In Tensorflow and JAX, for example, the tensor gets compiled to the intermediate XLA format (https://www.tensorflow.org/xla), then passed to the XLA complier (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla/service) or the new TFRT runtime (https://github.com/tensorflow/runtime/blob/master/documents/tfrt_host_runtime_design.md), or some more esoteric hardware (https://github.com/pytorch/glow).
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Esperanto Champions the Efficiency of Its 1,092-Core RISC-V Chip
The main reasons are hiring, and depth and breadth of the product.
Compilers are hard, device support is hard, the compiler community is small and closed source compilers quickly become weird tech islands.
https://github.com/pytorch/glow
- From Julia to Rust
What are some alternatives?
DIPs - D Improvement Proposals
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
Catlab.jl - A framework for applied category theory in the Julia language
serving - A flexible, high-performance serving system for machine learning models
IRTools.jl - Mike's Little Intermediate Representation
XLA.jl - Julia on TPUs
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
StaticArrays.jl - Statically sized arrays for Julia
SumTypes.jl - An implementation of Sum types in Julia
egg - egg is a flexible, high-performance e-graph library
proposals - ✍️ Tracking the status of Babel's implementation of TC39 proposals (may be out of date)
runtime - A performant and modular runtime for TensorFlow