approaches have been explored to address this issue, including Grouped-Query Attention (GQA) (Ainslie et al., 2023) and Multi-Query Attention (MQA) (Shazeer, 2019). However, these methods often compromise limit the inference efficiency. In order to reduce the KV cache, Multi-Query Atten- tion (MQA) (Shazeer, 2019) and Grouped-Query Attention (GQA) (Ainslie et al., 2023) are proposed. They require a smaller respectively: q? = ??h?, (1) k? = ? ?h?, (2) v? = ??h?, (3) 6 Grouped-Query Attention (GQA) Multi-Head Attention (MHA) Multi-Query Attention (MQA) Multi-Head Latent Attention (MLA) Keys Queries Values

lifecycle)  2012-2018 IBM Research – Almaden, USA  Declarative large-scale machine learning  Optimizer and runtime of Apache SystemML  2011 PhD TU Dresden, Germany  Cost-based optimization of integration integration flows  Systems support for time series forecasting  In-memory indexing and query processing Data Management Group DB group https://github.com/ apache/systemds 6 706.015 Introduction to Romulo Goncalves: An architecture for recycling intermediates in a column-store. SIGMOD 2009  Raw Query Processing  #8.1 Dominik Durner, Viktor Leis, Thomas Neumann: JSON Tiles: Fast Analytics on Semi-

original optimizer in a LossScale0ptimizer . loss_scale_optimizer = LossScaleOptimizer(opt，1oss_scale_manager) # Call minimize() on the loss scale optimizer. train_op = loss_scale_optimizer.minimize(1oss) PLATFORM INT8 Inference on PAI- 引FTe[= PAI-Blade Model Analysis Graph optimization Blade Graph Optimizer TensorRT Customized OptimizeT TAO Compiler (XLA) cuUBLAS/VcuDNNVCUTL， Blade Kernel Lib S，

Param Frontends Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning Automated Optimizer Schedule explorer Cost model Mali TOPI ROCM TOPI PVRTOPI Alibaba Al.Labs 阿里巴巴人工智能实验室 PVR TOPI

optimization potential benefit: 1.5x speedup Engineering intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate

(Highlight collisions mode only) - allows to establish a track even if is violating the DRC rules. Optimizer effort - defines how much time the router shall spend optimizing the routed/shoved traces. More

(Highlight collisions mode only) - allows to establish a track even if is violating the DRC rules. Optimizer effort - defines how much time the router shall spend optimizing the routed/shoved traces. More

mode only) - allows to establish a track even if is violating the DRC rules. Pcbnew 77 / 142 • Optimizer effort - defines how much time the router shall spend optimizing the routed/shoved traces. More

(Highlight collisions mode only) - allows to establish a track even if is violating the DRC rules. • Optimizer effort - defines how much time the router shall spend optimizing the routed/shoved traces. More

ps service, groupName is required field for this service. It will generate sql file with alter query statement for date-time and time field at location $\{ofbiz.home}/runtime/tempfiles/.sql You can that property does exist, the ? can be left out if (!fieldName?.property) {} // CAUTION: every query like this in Groovy evaluates to a Boolean type // everything that is empty or false will turn into def query = from( "ProductCategoryMember").where("productC ategoryId", parameters. productCategoryId) if (parameters.validDate) { query.filterByDate() } List productCategoryMembers = query .queryList()