Highlights
- Cassandra with Intel® QuickAssist Technology (Intel® QAT) hardware-accelerated compression
- Leveraging Intel AMX acceleration with TensorFlow for different AI Use-Cases such as NLP (using BERT-Large), Graph Neural Networks (using RGAT) and Computer Vision (using ResNet50 v1.5)
- Fortio-Envoy Optimization guide: best known practices to optimize Envoy TCP proxy performance
- Scikit-learn Optimization guide: best practices for optimal performance in machine learning workflows that use the scikit-learn Python library
- zlib-accel: Transparent Hardware-Accelerated Compression for zlib on Intel® Xeon® Processors
- HPC: Recipe for SIMULIA PowerFLOW application on Intel® Xeon® 6 with P-Cores
What's Changed
- Fix predicate for updating global flag by @GaneshRapolu in #26
- Cassandra QAT update optimization by @ssherman8 in #25
- Update HPC README.md by @NadyaTen in #27
- Add AMX with TensorFlow quick-start guides and AI use-case examples by @othakkar in #28
- Add Fortio-Envoy optimization guide by @vaibhavk2 in #29
- Add summary page for zlib-accel by @missa-prime in #33
- Add guide for scikit-learn by @david-cortes-intel in #31
- Update scikit-learn README.md to fix github pages rendering issue. by @rsiyer-intel in #35
New Contributors
- @GaneshRapolu made their first contribution in #26
- @ssherman8 made their first contribution in #25
- @othakkar made their first contribution in #28
- @vaibhavk2 made their first contribution in #29
- @missa-prime made their first contribution in #33
- @david-cortes-intel made their first contribution in #31
Full Changelog: v1.0.0...v1.1.0
Contributors
missa-prime, GaneshRapolu, and 6 other contributors