Gela Malek Pour gmalekpour@esri.com Visualize Time Enabled Data using ArcGIS Qt (C++) and ToolkitAbout Esri • We build mapping technology that our customers use to solve the world’s most complex challenges offer solutions to apply location-based analytics to business practices - Visualize and analyze data more effectively - Collaborate and share maps, apps and reports easily • Headquartered in Southern time enabled data • Setup the development environment using Esri templates • Use the ArcGIS toolkit to use already existing UI componentsTime aware layer • We will be using a feature layer that include

Using the Microsoft Graph API to get Office 365 data in your mobile apps Alex Ziskind Technical Director @digitalix www.nuvious.com Hi, I’m Alex From + 750 Million 50 Million per month Problems

第三届中国 Rust 开发者大会 Use Rust to Develop the Decentralized Open Data Application Mike Tang daogangtang@gmail.com @daogangtang 2023-06-08 ➔ 裁员 ➔ 互联网格局定型 ➔ 平台倒闭，数据丢失 这是一个什么时代？ 互联网的终局 创业 -> 种子 Weapons Web3 Utilizes For mainstream tech stack, e. g. Ethereum ➔ A world-wide unified network to provide a serverless layer • A single-core CPU machine • Is serverless really the next stage of Internet dataset, data will increase quickly on any dimension. ➔ Multiple dimensions Dataset Models ➔ What is open data? Build an internet platform, but open its data to everyone. ➔ Why we need open data? The

Runs on V8 JavaScript VM Runs on JavaScriptCore VM NativeScript Module Layer (NML) o Abstractions on native APIs provide unified, cross-platform API o Dozens available out of the box o Easy for developers developers to add o All native APIs still available at JavaScript layer for platform-specific scenarios o NativeScript modules follow Node module conventions (CommonJS) Example: NativeScript file module

the PCI-Express bus.” -- https://developer.nvidia.com/blog/unified-memory-in-cuda- 6/ CPU vs GPU Memory System Memory GPU Memory 12 |“Unified Memory creates a pool of managed memory that is shared between the system automatically migrates data allocated in Unified Memory between host and device...” -- https://developer.nvidia.com/blog/unified-memory-in-cuda-6/ Unified Memory System Memory GPU Memory • … std::pmr 16 |// gpu unified_memory_resource mem; std::pmr::vector x(N, &mem); std::pmr::vector y(N, &mem); // … Memory Allocation 17 |struct unified_memory_resource : std::pmr::memory_resource

"Rust-based Container Runtimes" is coming soon. 2.3 Unified runtime for eBPF and Wasm Summary II. Practicing Sparrow  Our previous talks "GraalVM-based unified runtime for eBPF & Wasm" at GOTC 2021 (Shenzhen) & "Revisiting GraalVM-based unified runtime for eBPF & Wasm" at OpenInfra Days China 2021(Beijing), and the third-round discussion of this topic will come in this year, RECEIVE_DATA and writing to TRANSMIT_DATA were implemented in Rust: III. Sparrow Development Take a look at how the reading from RECEIVE_DATA and writing to TRANSMIT_DATA were

会自动进行同步操作 ，即和 cudaDeviceSynchronize() 等价！ 因此前面的 cudaDeviceSynchronize() 实 际上可以删掉了。 统一内存地址技术（ Unified Memory ） • 还有一种在比较新的显卡上支持的特性， 那就是统一内存 (managed) ，只需把 cudaMalloc 换成 cudaMallocManaged 即可，释放时也是通过 vector 对元素的构造。默认情况下他可以有任意多个参数，而 如果没有参数则说明是无参构造函数。 • 因此我们只需要判断是不是有参数，然后是不是传统的 C 语 言类型（ plain-old-data ），如果是，则跳过其无参构造，从 而避免在 CPU 上低效的零初始化。 进一步：核函数可以是一个模板函数 • 刚刚说过 CUDA 的优势在于对 C++ 的 完全支持。所以 __global__ 整个地拷贝到 GPU 上！ 而不是浅拷贝其起始地址指针。 如何捕获外部变量？ • 正确的做法是先获取 arr.data() 的值到 arr_data 变量，然后用 [=] 按值捕获 arr_data ，函数体里面也通过 arr_data 来访问 arr 。 • 为什么这样？因为 data() 返回一个起始地 址的原始指针，而原始指针是浅拷贝的， 所以可以拷贝到 GPU 上，让他访问。这

 Several clocks available in :  std::system_clock: wall clock time from system-wide real-time clock  std::steady_clock: guarantees it never goes backwards  std::high_resolution_clock:

Time-sharing • Preserves immediacy, and hence enables us to maintain an overview of complexity • Unified programming environment • They attack the accidental difficulties of using tools togetherHopes for

reference count atomic ref_count … std::atomic ctrl Introduce a second layer of reference counting on the atomic instance itself! struct counted_cb { control_block* Measurement: Throughput vs latency? • Workload: Proportion of reads vs writes • Hotness: Does the data fit in cache? • Contention: How many threads operation on the same location? • We will benchmark