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PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.
An example of a roofline model in its basic form. As the image shows, the curve consists of two platform-specific performance ceilings: the processor's peak performance and a ceiling derived from the memory bandwidth. Both axes are in logarithmic scale
In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...
In computing, an input–output memory management unit (IOMMU) is a memory management unit (MMU) connecting a direct-memory-access–capable (DMA-capable) I/O bus to the main memory. Like a traditional MMU, which translates CPU -visible virtual addresses to physical addresses , the IOMMU maps device-visible virtual addresses (also called device ...
Flat memory model or linear memory model refers to a memory addressing paradigm in which "memory appears to the program as a single contiguous address space." [1] The CPU can directly (and linearly) address all of the available memory locations without having to resort to any sort of bank switching, memory segmentation or paging schemes.
The read-of-non-persistent-write problem is found for lock-free programs on persistent memory. As compare-and-swap (CAS) operations do not persist the written values to persistent memory, the modified data can be made visible by the cache coherence protocol to a concurrent observer before the modified data can be observed by a crash observer at persistent memory.
To enable handling long data sequences, Mamba incorporates the Structured State Space sequence model (S4). [2] S4 can effectively and efficiently model long dependencies by combining continuous-time, recurrent, and convolutional models. These enable it to handle irregularly sampled data, unbounded context, and remain computationally efficient ...
Other languages, such as C and C++, were designed for use with manual memory management, but have garbage-collected implementations available. Some languages, like Ada, Modula-3, and C++/CLI, allow both garbage collection and manual memory management to co-exist in the same application by using separate heaps for collected and manually managed ...