© 2019 Apex.AI, Inc. Safe and certified software for autonomous mobility TM Practical memory pool based allocators for Modern C++ by Misha Shalem misha.shalem@apex.ai © 2020 Apex.AI, Inc.● CppCon shall only be allocated and deallocated during non-realtime program phases © 2020 Apex.AI, Inc.Memory pool ● Memory pools with fixed block sizes is a solution which addresses these issues: ○ It provides modifications but allocate from a memory pool under the hood © 2020 Apex.AI, Inc.Implementation Components: 1. A memory pool 2. An allocator which allocates from a memory pool 3. A way to generate bucket definitions

y): return x + y Handlers execute(self, request, pool, loglevel, logfile, **kw): Parameters • request – A TaskRequest. • pool – The task pool. • loglevel – Current loglevel. • logfile – Name of for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted. Make your design asynchronous instead, for example by using callbacks. Bad: @task Automatic Pool Support In version 2.3 there is now support for automatic connection pools, so you don’t have to manually handle connections and publishers to reuse connections. See the BROKER_POOL_LIMIT

y): return x + y Handlers execute(self, request, pool, loglevel, logfile, **kw): Parameters: request – A TaskRequest. pool – The task pool. loglevel – Current loglevel. logfile – Name of the currently for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted. Make your design asynchronous instead, for example by using callbacks. Bad: @task Automatic Pool Support In version 2.3 there is now support for automatic connection pools, so you don’t have to manually handle connections and publishers to reuse connections. See the BROKER_POOL_LIMIT

y): return x + y Handlers execute(self, request, pool, loglevel, logfile, **kw): Parameters: request – A TaskRequest. pool – The task pool. loglevel – Current loglevel. logfile – Name of the currently for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted. Make your design asynchronous instead, for example by using callbacks. Bad: @task Automatic Pool Support Since version 2.3 there is support for automatic connection pools, so you don’t have to manually handle connections and publishers to reuse connections. The connection pool is enabled

y): return x + y Handlers execute(self, request, pool, loglevel, logfile, **kw): Parameters: request – A TaskRequest. pool – The task pool. loglevel – Current loglevel. logfile – Name of the currently for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted. Make your design asynchronous instead, for example by using callbacks. Bad: @task Automatic Pool Support In version 2.3 there is now support for automatic connection pools, so you don’t have to manually handle connections and publishers to reuse connections. See the BROKER_POOL_LIMIT

for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted. Make your design asynchronous instead, for example by using callbacks. Bad: @task Runtime is the time it took to execute the task using the pool. (Starting from the task is sent to the worker pool, and ending when the pool result handler callback is called). task-failed(uuid, exception or as a small part of a larger application. Celery supports Eventlet as an alternative execution pool implementation. It is in some cases superior to multiprocessing, but you need to ensure your tasks

Error e-mails Can be configured to send e-mails to the administrators when tasks fails. Supervised Pool workers are supervised and automatically replaced if they crash. Documentation The latest documentation for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted. Make your design asynchronous instead, for example by using callbacks. Bad: @task() Runtime is the time it took to execute the task using the pool. (Starting from the task is sent to the worker pool, and ending when the pool result handler callback is called). task-failed(uuid, exception

optimized settings). Flexible Almost every part of Celery can be extended or used on its own, Custom pool implementations, serializers, compression schemes, logging, schedulers, consumers, producers, broker for using the eventlet [https://pypi.python.org/pypi/eventlet/] pool. for using the gevent [https://pypi.python.org/pypi/gevent/] pool. Transports and Backends for using the librabbitmq C library. CPU’s is rarely effective, and likely to degrade performance instead. Including the default prefork pool, Celery also supports using Eventlet, Gevent, and running in a single thread (see Concurrency). –

optimized settings). Flexible Almost every part of Celery can be extended or used on its own, Custom pool implementations, serializers, compression schemes, logging, schedulers, consumers, producers, broker for using the eventlet [https://pypi.python.org/pypi/eventlet/] pool. for using the gevent [https://pypi.python.org/pypi/gevent/] pool. Transports and Backends for using the librabbitmq C library. CPU’s is rarely effective, and likely to degrade performance instead. Including the default prefork pool, Celery also supports using Eventlet, Gevent, and running in a single thread (see Concurrency). –

optimized settings). Flexible Almost every part of Celery can be extended or used on its own, Custom pool implementations, serializers, compression schemes, logging, schedulers, consumers, producers, broker for using the eventlet [https://pypi.python.org/pypi/eventlet/] pool. for using the gevent [https://pypi.python.org/pypi/gevent/] pool. Transports and Backends for using the librabbitmq C library. CPU’s is rarely effective, and likely to degrade performance instead. Including the default prefork pool, Celery also supports using Eventlet, Gevent, and running in a single thread (see Concurrency). –