算法的并行化 Ø CPU: 多核 CPU Ø GPU： 多核处理器 Ø 硬件仿真：算法计算在FPGA里实现，输入和输出在 PC 端实现。 Ø Hardware in the loop simulation 加速计算 (Accelerate Computing) 多核处理器系统 8 Ø HDL：硬件描述语言 (Verilog, VHDL)，通过与或非 门、触发器等逻辑电路实现一些特定的功能。 基本架构 9 Application Processor 64-bit Dual/Quad-Core 赛灵思 Zynq UltraScale+ MPSoC (16nm SOC 芯片） Real-Time Processors 32-bit Dual-Core Platform & Power Management Granular Power Control Functional Safety

算法的并行化 ➢ CPU: 多核 CPU ➢ GPU： 多核处理器 ➢ 硬件仿真：算法计算在FPGA里实现，输入和输出在 PC 端实现。 ➢ Hardware in the loop simulation 加速计算 (Accelerate Computing) 多核处理器系统 8 ➢ HDL：硬件描述语言 (Verilog, VHDL)，通过与或非 门、触发器等逻辑电路实现一些特定的功能。 基本架构 9 Application Processor 64-bit Dual/Quad-Core 赛灵思 Zynq UltraScale+ MPSoC (16nm SOC 芯片） Real-Time Processors 32-bit Dual-Core Platform & Power Management Granular Power Control Functional Safety

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML calculate_key(target) method applied to the target objects. This weak value dictionary is used to short-circuit creation so that multiple references to the same (object, function) pair produce the same BoundMethodWeakref

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or calculate_key(target) method applied to the target objects. This weak value dictionary is used to short-circuit creation so that multiple references to the same (object, function) pair produce the same BoundMethodWeakref

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML calculate_key(target) method applied to the target objects. This weak value dictionary is used to short-circuit creation so that multiple references to the same (object, function) pair produce the same BoundMethodWeakref

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or calculate_key(target) method applied to the target objects. This weak value dictionary is used to short-circuit creation so that multiple references to the same (object, function) pair produce the same BoundMethodWeakref

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML messages about what the worker is doing. These messages are called “events”. The events are used by real-time monitors to show what the cluster is doing, but they are not very useful for monitoring over a longer

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML messages about what the worker is doing. These messages are called “events”. The events are used by real-time monitors to show what the cluster is doing, but they are not very useful for monitoring over a longer

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or messages about what the worker is doing. These messages are called “events”. The events are used by real-time monitors to show what the cluster is doing, but they are not very useful for monitoring over a longer

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or messages about what the worker is doing. These messages are called “events”. The events are used by real-time monitors to show what the cluster is doing, but they are not very useful for monitoring over a longer