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| 001 | 1034769 | ||
| 005 | 20250203103359.0 | ||
| 024 | 7 | _ | |a 10.5281/ZENODO.14001852 |2 doi |
| 037 | _ | _ | |a FZJ-2024-07524 |
| 100 | 1 | _ | |a Hoppe, Fabian |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Heat (v1.5.0) |
| 250 | _ | _ | |a 1.5.0 |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Software |2 DCMI |
| 336 | 7 | _ | |a Software |b sware |m sware |0 PUB:(DE-HGF)33 |s 1736770887_16364 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Computer Program |0 6 |2 EndNote |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Software |2 DataCite |
| 520 | _ | _ | |a Heat 1.5 Release Notes Overview Highlights Performance Improvements Sparse Signal Processing RNG Statistics Manipulations I/O Machine Learning Deep Learning Other Updates Contributors Overview With Heat 1.5 we release the first set of features developed within the ESAPCA project co-funded by the European Space Agency (ESA). The main focus of this release is on distributed linear algebra operations, such as tall-skinny SVD, batch matrix multiplication, and triangular solver. We also introduce vectorization via vmap across MPI processes, and batch-parallel random number generation as default for distributed operations. This release also includes a new class for distributed Compressed Sparse Column matrices, paving the way for future implementation of distributed sparse matrix multiplication. On the performance side, our new array redistribution via MPI Custom Datatypes provides significant speed-up in operations that require it, such as FFTs. We are grateful to our community of users, students, open-source contributors, the European Space Agency and the Helmholtz Association for their support and feedback. Highlights [ESAPCA] Distributed tall-skinny SVD: ht.linalg.svd (by @mrfh92) Distributed batch matrix multiplication: ht.linalg.matmul (by @FOsterfeld) Distributed solver for triangular systems: ht.linalg.solve_triangular (by @FOsterfeld) Vectorization across MPI processes: ht.vmap (by @mrfh92) Other Changes Performance Improvements #1493 Redistribution speed-up via MPI Custom Datatypes available by default in ht.resplit (by @JuanPedroGHM) Sparse #1377 New class: Distributed Compressed Sparse Column Matrix ht.sparse.DCSC_matrix() (by @Mystic-Slice) Signal Processing #1515 Support batch 1-d convolution in ht.signal.convolve (by @ClaudiaComito) RNG #1508 Introduce batch-parallel RNG as default for distributed operations (by @mrfh92) Statistics #1420 Support sketched percentile/median for large datasets with ht.percentile(sketched=True) (and ht.median) (by @mrhf92) #1510 Support multiple axes for distributed ht.percentile and ht.median (by @ClaudiaComito) Manipulations #1419 Implement distributed unfold operation (by @FOsterfeld) I/O #1602 Improve load balancing when loading .npy files from path (by @Reisii) #1551 Improve load balancing when loading .csv files from path (by @Reisii) Machine Learning #1593 Improved batch-parallel clustering ht.cluster.BatchParallelKMeans and ht.cluster.BatchParallelKMedians (by @mrfh92) Deep Learning #1529 Make dataset.ishuffle optional. (by @krajsek) Other Updates #1618 Support mpi4py 4.x.x (by @JuanPedroGHM) Contributors @mrfh92, @FOsterfeld, @JuanPedroGHM, @Mystic-Slice, @ClaudiaComito, @Reisii, @mtar and @krajsek |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 700 | 1 | _ | |a Osterfeld, Fynn |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Gutiérrez Hermosillo Muriedas, Juan Pedro |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Vaithinathan Aravindan, Ashwath |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Comito, Claudia |0 P:(DE-Juel1)174573 |b 4 |u fzj |
| 700 | 1 | _ | |a Krajsek, Kai |0 P:(DE-Juel1)129347 |b 5 |u fzj |
| 700 | 1 | _ | |a Nguyen Xuan, Tu |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Tarnawa, Michael |0 P:(DE-Juel1)178977 |b 7 |u fzj |
| 700 | 1 | _ | |a Coquelin, Daniel |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Debus, Charlotte |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Götz, Markus |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Hagemeier, Björn |0 P:(DE-Juel1)132123 |b 11 |u fzj |
| 700 | 1 | _ | |a Knechtges, Philipp |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Rüttgers, Alexander |0 P:(DE-HGF)0 |b 13 |
| 773 | _ | _ | |a 10.5281/ZENODO.14001852 |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 1 |
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