distributed

Can Haskell functions be serialized?

由 冷暖自知 提交于 2019-11-27 15:37:42
问题 The best way to do it would be to get the representation of the function (if it can be recovered somehow). Binary serialization is preferred for efficiency reasons. I think there is a way to do it in Clean, because it would be impossible to implement iTask, which relies on that tasks (and so functions) can be saved and continued when the server is running again. This must be important for distributed haskell computations. I'm not looking for parsing haskell code at runtime as described here:

What are the differences between Tibco EMS and Rendezvous

由 杀马特。学长 韩版系。学妹 提交于 2019-11-27 10:52:27
问题 What are some of the key differences between these two technologies? Does one have obvious advantages over the other? 回答1: RV is like a radio broadcaster and EMS is like a telephone. If you want to send a message to everyone in town (e.g. the weather forecast for today) then a radio is good because one message goes to everyone simultaneously. Telephone is bad because it takes a long time to call everyone and you pay 20c a call. If you want to tell someone your credit card number you would use

alternative to memcached that can persist to disk

由 拜拜、爱过 提交于 2019-11-27 10:51:33
I am currently using memcached with my java app, and overall it's working great. The features of memcached that are most important to me are: it's fast, since reads and writes are in-memory and don't touch the disk it's just a key/value store (since that's all my app needs) it's distributed it uses memory efficiently by having each object live on exactly one server it doesn't assume that the objects are from a database (since my objects are not database objects) However, there is one thing that I'd like to do that memcached can't do. I want to periodically (perhaps once per day) save the cache

How does asynchronous training work in distributed Tensorflow?

由 微笑、不失礼 提交于 2019-11-27 09:53:28
问题 I've read Distributed Tensorflow Doc, and it mentions that in asynchronous training, each replica of the graph has an independent training loop that executes without coordination. From what I understand, if we use parameter-server with data parallelism architecture, it means each worker computes gradients and updates its own weights without caring about other workers updates for distributed training Neural Network. As all weights are shared on parameter server (ps), I think ps still has to

Anatomy of a Distributed System in PHP

由 天涯浪子 提交于 2019-11-27 09:35:23
问题 I've a problem which is giving me some hard time trying to figure it out the ideal solution and, to better explain it, I'm going to expose my scenario here. I've a server that will receive orders from several clients. Each client will submit a set of recurring tasks that should be executed at some specified intervals, eg.: client A submits task AA that should be executed every minute between 2009-12-31 and 2010-12-31 ; so if my math is right that's about 525 600 operations in a year, given

how to run tensorflow distributed mnist example

由 丶灬走出姿态 提交于 2019-11-27 02:22:11
问题 I am new to distributed tensorflow. I found this distributed mnist test in here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dist_test/python/mnist_replica.py But I don't know how to make it run. I used the following script: python distributed_mnist.py --num_workers=3 --num_parameter_servers=1 --worker_index=0 --worker_grpc_url="grpc://tf-worker0:2222"\ & python distributed_mnist.py --num_workers=3 --num_parameter_servers=1 --worker_index=1 --worker_grpc_url="grpc:/

In Apache Kafka why can't there be more consumer instances than partitions?

由 时间秒杀一切 提交于 2019-11-27 00:08:26
问题 I'm learning about Kafka, reading the introduction section here https://kafka.apache.org/documentation.html#introduction specifically the portion about Consumers. In the second to last paragraph in the Introduction it reads Kafka does it better. By having a notion of parallelism—the partition—within the topics, Kafka is able to provide both ordering guarantees and load balancing over a pool of consumer processes. This is achieved by assigning the partitions in the topic to the consumers in

Distributed tensorflow: the difference between In-graph replication and Between-graph replication

由 妖精的绣舞 提交于 2019-11-26 23:53:42
I got confused about the two concepts: In-graph replication and Between-graph replication when reading the Replicated training in tensorflow's official How-to. It's said in above link that In-graph replication. In this approach, the client builds a single tf.Graph that contains one set of parameters (in tf.Variable nodes pinned to /job:ps); ... Does this mean there are multiple tf.Graph s in Between-graph replication approach? If yes, where are the corresponding codes in the provided examples? While there is already a Between-graph replication example in above link, could anyone provide a In

Best way to aggregate multiple log files from several servers [closed]

由 安稳与你 提交于 2019-11-26 22:35:44
问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 6 years ago . I need a simple way to monitor multiple text log files distributed over a number of HP-UX servers. They are a mix of text and XML log

Web Services vs EJB vs RMI, advantages and disadvantages?

由 心已入冬 提交于 2019-11-26 18:44:49
问题 My web server would be overloaded quickly if all the work were done there. I'm going to stand up a second server behind it, to process data. What's the advantage of EJB over RMI, or vice versa? What about web services (SOAP, REST)? 回答1: EJBs are built on top of RMI. Both imply Java clients and beans. If your clients need to be written in something else (e.g., .NET, PHP, etc.) go with web services or something else that speaks a platform-agnostic wire protocol, like HTTP or XML over HTTP or
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