projected onto the ground, for planning purposes 75 https://docs.nav2.org/setup_guides/footprint/setup_footprint.htmlgithub.com/griswaldbrooks/spanny2 layout_polygonal struct layout_polygonal::mapping std::sin(theta) + v.y * std::cos(theta) + p.y; v.x = x; v.y = y; } // create filled footprint return convex_fill(vertices, {parent_shape_.extent(0), parent_shape_ com/griswaldbrooks/spanny2 driving robot can visualize robot in map path planner will use this footprint to check for collisions 77github.com/griswaldbrooks/spanny2 few! that was a lot • learned what

Person C++ Under the Hood – Inheritance vs. Aggregation 109C++ Under the Hood Object Memory Footprint v-table Mechanism 110C++ Under the Hood 111C++ Under the Hood 112C++ Under the Hood 113C++ Under Constructor/Destructor Order 165 godbolt.org/z/3vahsaEr9C++ Under the Hood - structure static footprint 166 godbolt.org/z/563sbr99rC++ Under the Hood - Pre-/Post-main() TODO 167 godbolt.org/z/xxxxxxxxx

huge pages) Case study: THP (transparent huge pages) Description ● The TiDB server memory footprint is abnormal ● The memory available on this node is not too much Description ● The Go Runtime

only : micrograd++ comes with microgradpp.h, that contains all the headers in one file. • Small footprint : libmicrogradpp.a is only 122KB. • Inspiration from micrograd: Drawing inspiration from the simplicity

hypervisor to solve issues now VM composition guidelines • Assuming you workload fits with the footprint of a single node, compose worker node VMs as “walled gardens” corresponding to node size • Specify

Design • We now have a candidate design for a data structure that radically reduces the memory footprint from our original example, while retaining the same functionality • This works nicely to solve

std::string Bar std::string Bar std::stringObject Introspection (OI) Goals • Byte level memory footprint analysis for objects • Complete object type hierarchies • Dynamic allocations and containers

lib vs Static Lib • Both are mechanisms for code sharing • Static: 1. Smaller single binary footprint (only used sections are imported) 2. Better code locality 3. Efficient calls • Dynamic: 1. Shared

scenes (compiler and ASan runtime) and analyze the instrumentation impact, both in perf and memory footprint. We’ll examine a handful of examples diagnosed by ASan and see how easy it is to read memory snapshots

Mixed static/dynamic Well, it’s a little bit more complicated. . . Optimization goal Memory footprint of only dynamic dimensions Optimal runtime access of a particular dimension Bonus: optimized compilation