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Parallelizing Large-Scale Tensor Network Contraction on Multiple GPUs

arXiv:2606.01852v1 Announce Type: new Abstract: Exact tensor network contraction underpins quantum circuit simulation, quantum error correction, combinatorial optimization, and many-body dynamics. The dominant parallelization strategy, slicing, scales exponentially and incurs redundant computation. We present a multi-GPU framework that instead distributes intermediate tensors across devices with explicit communication, converting a fixed contraction path into a communication-efficient...

I Put a Datacenter GPU in My Gaming PC for £200

I Put a Datacenter GPU in My Gaming PC for £200 I already had an RTX 4080. Good enough for gaming, not good enough for the models I wanted to run locally. The next step up in GPU land is either spend a fortune on a card with more VRAM, or find another way.

Magnum.np.distributed: Accelerating Finite Difference Micromagnetic Simulations with Multiple GPUs

Announce Type: new Abstract: Micromagnetic simulations are essential tools in nanomagnetism and spintronics research. Although widely adopted solvers like Mumax3 and the Python-native magnum.np use GPU acceleration to improve performance, these tools are limited to single-device computation. In this work, we present the first Python-native multi-GPU micromagnetic framework by extending magnum.np with PyTorch Distributed.

ACEAPEX: Parallel LZ77 Decoding via Encode-Time Absolute Offset Resolution

arXiv:2606.04268v1 Announce Type: new Abstract: LZ77-based codecs exhibit a fundamental sequential bottleneck in decoding: each back-reference depends on previously decompressed data, preventing multi-core scaling. We present ACEAPEX, a parallel LZ77 codec that stores all back-references as absolute positions in the decompressed output and organizes data into self-contained 1 MB blocks, enabling embarrassingly parallel block-level decoding. Integrated into lzbench, ACEAPEX achieves 10,160...