clear grey pixels are unknown 42github.com/griswaldbrooks/spanny2 occupancy grid 43 struct occupancy_grid_t { occupancy_grid_t(std::dextents shape, unsigned char value grid_{data_.data(), shape} {} private: std::vector data_; std::mdspan> grid_; };github.com/griswaldbrooks/spanny2 occupancy grid 44 struct occupancy_grid_t { occupancy_grid_t(std::dextents shape, unsigned char value = 127) : data_(shape.extent(0) * shape.extent(1), value), grid_{data_.data(),

notesINTRODUCING THE GRID CACHE • Stores static world elements for quick queries • Consider it a sparse 3D array • Process to create: ▪ Partition 3D space into cube grid ▪ Distribute items into grid cells ▪ Store things - but nothing like this Can think of it as sparse 3D array Partition 3d space into fixed sized grid Put from those locations items into cells Store non-empty cells in a map - using array index as key each axis float grid_extent; // Grid extent from origin float cell_size; // Size of cell uint32_t CalculateAxisOffset(float value) { value = floor(clamp(value, -grid_extent, grid_extent)); return

float* x; float* y; // … int block_size = 256; int grid_size = (N + block_size - 1) / block_size; add_gpu<<<grid_size, block_size>>>(N, x, y); // … } Ok, about the kernel parameters += stride) y[i] = x[i] + y[i]; } TEST_CASE("cppcon-1", "[CUDA]") { // … add_gpu<<<grid_size, block_size>>>(N, x, y); // … } Ok, about the kernel parameters 10 |Memory“In a typical PC

increasing – and its progress is increasingly bottlenecked not by data or algorithms, but by the grid and strains related to demand. While AI presently places considerable demands on the energy sector hardware efficiency are critical to offset the strain of increasing AI and internet usage on our grid. So far, though, they have not been enough. This trend aligns with Jevons Paradox, first proposed This is driving new focus on expanding energy production capacity – and new questions about the grid’s ability to manage. Yet again, we see this as one of the perpetual ‘a-ha’s’ of technology: costs

again visualize memory ● This time I’m going to show a grid ○ Still a linear array of addresses as indicated by the arrows. ○ I am using a grid so I can fit more memory on the screen. *Assume 1 byte

improve Overall Equipment Effectiveness (OEE). Energy companies must balance EV charger loads and manage grid performance in real time. Banks need to analyze audit logs from their website and application in real

shared resource (again, they cannot cross the intersection) 134Concurrency Caveat #3 | Deadlock ● Grid lock in a traffic jam ○ Each car prevents others from going through a shared resource (the intersection)

Kernel invocations: work-items ● Work-items collected into work-groups, forming a 1-3 NDimensional grid of work-items ● Work-items can only synchronize within work-group ● Work-items collected into 1D

Other Plugins • djangocms-link • djangocms-snippet • djangocms-style • djangocms-column • djangocms-grid • djangocms-oembed • djangocms-table File and image handling • Django Filer for file and image management

"djangocms_frontend.contrib.collapse", "djangocms_frontend.contrib.content", "djangocms_frontend.contrib.grid", "djangocms_frontend.contrib.image", "djangocms_frontend.contrib.jumbotron", "djangocms_frontend