公开(公告)号：US09864589B2

公开(公告)日：2018-01-09

申请号：US15499656

申请日：2017-04-27

申请人： Facebook, Inc.

发明人： Jeffrey S. Dunn ,  Rafael L. Sagula

IPC分类号： G06F9/45 ,  G06F9/455

CPC分类号： G06F8/443 ,  G06F8/41 ,  G06F9/45508 ,  G06F9/45516

摘要： In one embodiment, a method includes generating an executable version of a decision tree by compiling source code into executable code and verifying the executable code by comparing a result of executing the executable code with a result of evaluating the decision tree in interpreted mode. The method further includes replacing the decision tree evaluated in the interpreted mode with the executable code if the executable code is verified or discarding the executable code otherwise.

公开(公告)号：US09116720B2

公开(公告)日：2015-08-25

申请号：US14511628

申请日：2014-10-10

申请人： Facebook, Inc.

发明人： Jeffrey S. Dunn ,  Rafael L. Sagula

IPC分类号： G06F9/45 ,  G06F9/455

CPC分类号： G06F8/443 ,  G06F8/41 ,  G06F9/45508 ,  G06F9/45516

摘要： In one embodiment, a decision tree is evaluated in interpreted mode while statistics are collected. The decision tree is then represented as source code, and each decision in the decision tree is annotated with instructions determined based on the collected statistics. The source code is compiled into machine code, and the machine code is optimized based on the instructions annotating each decision in the decision tree.

摘要翻译： 在一个实施例中，在解析模式下评估决策树，同时收集统计信息。 然后将决策树表示为源代码，并且使用根据所收集的统计信息确定的指令对决策树中的每个决策进行注释。 源代码被编译成机器代码，并且机器代码基于在决策树中注释每个决定的指令进行优化。

公开(公告)号：US20150026670A1

公开(公告)日：2015-01-22

申请号：US14511628

申请日：2014-10-10

申请人： Facebook, Inc.

发明人： Jeffrey S. Dunn ,  Rafael L. Sagula

IPC分类号： G06F9/45 ,  G06F9/455

CPC分类号： G06F8/443 ,  G06F8/41 ,  G06F9/45508 ,  G06F9/45516

摘要： In one embodiment, a decision tree is evaluated in interpreted mode while statistics are collected. The decision tree is then represented as source code, and each decision in the decision tree is annotated with instructions determined based on the collected statistics. The source code is compiled into machine code, and the machine code is optimized based on the instructions annotating each decision in the decision tree.

摘要翻译： 在一个实施例中，在解析模式下评估决策树，同时收集统计信息。 然后将决策树表示为源代码，并且使用根据所收集的统计信息确定的指令对决策树中的每个决策进行注释。 源代码被编译成机器代码，并且机器代码基于在决策树中注释每个决定的指令进行优化。

公开(公告)号：US20170228224A1

公开(公告)日：2017-08-10

申请号：US15499656

申请日：2017-04-27

申请人： Facebook, Inc.

发明人： Jeffrey S. Dunn ,  Rafael L. Sagula

IPC分类号： G06F9/45 ,  G06F9/455

CPC分类号： G06F8/443 ,  G06F8/41 ,  G06F9/45508 ,  G06F9/45516

摘要： In one embodiment, a method includes generating an executable version of a decision tree by compiling source code into executable code and verifying the executable code by comparing a result of executing the executable code with a result of evaluating the decision tree in interpreted mode. The method further includes replacing the decision tree evaluated in the interpreted mode with the executable code if the executable code is verified or discarding the executable code otherwise.

公开(公告)号：US09678730B2

公开(公告)日：2017-06-13

申请号：US14740005

申请日：2015-06-15

申请人： Facebook, Inc.

发明人： Jeffrey S. Dunn ,  Rafael L. Sagula

IPC分类号： G06F9/45 ,  G06F9/455

CPC分类号： G06F8/443 ,  G06F8/41 ,  G06F9/45508 ,  G06F9/45516

摘要： In one embodiment, a decision tree is evaluated in interpreted mode while statistics are collected. The decision tree is then represented as source code, and each decision in the decision tree is annotated with instructions determined based on the collected statistics. The source code is compiled into machine code, and the machine code is optimized based on the instructions annotating each decision in the decision tree.

公开(公告)号：US20150277878A1

公开(公告)日：2015-10-01

申请号：US14740005

申请日：2015-06-15

申请人： Facebook, Inc.

发明人： Jeffrey S. Dunn ,  Rafael L. Sagula

IPC分类号： G06F9/45 ,  G06F9/455

CPC分类号： G06F8/443 ,  G06F8/41 ,  G06F9/45508 ,  G06F9/45516

摘要： In one embodiment, a decision tree is evaluated in interpreted mode while statistics are collected. The decision tree is then represented as source code, and each decision in the decision tree is annotated with instructions determined based on the collected statistics. The source code is compiled into machine code, and the machine code is optimized based on the instructions annotating each decision in the decision tree.