Computing power of computing cluster

1. Day trading is the day to buy the day you can sell
day trading is a way of foreign exchange trading. Usually, day trading refers to setting up and clearing positions on the same trading day. You can make several or even hundreds of such transactions in one day. It's up to you. There are four main steps for day trading through HPC: deciding to conct a foreign exchange transaction; Determine the currency and amount of the transaction and establish a position in your online account; Monitor your position; Close the position
If a friend wants to obtain additional income through foreign exchange,
it is suggested that a friend apply to become an HPC agent
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please contact me for the specific application process
I can help you strive for the highest standard
I wish you a happy transaction!

2. High performance computing (HPC) refers to the computing system and environment that usually uses many processors (as a part of a single machine) or several computers organized in a cluster (operating as a single computing resource)
there are many types of HPC systems, ranging from large clusters of standard computers to highly specialized hardware. Most cluster based HPC systems use high-performance network interconnection, such as those from Infiniband or myrinet
the basic network topology and organization can use a simple bus topology. In a high performance environment, the mesh network system provides a shorter latency between hosts, so it can improve the overall network performance and transmission rate!

3.
capacity Scheler supports the following features:
(1) computing power guarantee. Support multiple queues, a job can be submitted to a queue. Each queue is configured with a certain proportion of computing resources, and all jobs submitted to the queue share the resources in the queue< br />(2) Flexibility. Idle resources will be allocated to those queues that do not reach the upper limit of resource usage. When a queue that does not reach the upper limit of resource needs resources, once there are idle resources, they will be allocated< br />(3) Priority is supported. Queue supports job priority scheling (FIFO by default)
(4) Multiple leases. Considering a variety of constraints to prevent a single job, user or queue from monopolizing the resources in the queue or cluster< br />(5) Resource based scheling. It supports resource intensive jobs, allows jobs to use more resources than the default value, and can accommodate jobs with different resource requirements. However, only memory resource scheling is currently supported< br />3. Analysis of computing power scheler algorithm
3.1 The variables involved
in capacity, there are three kinds of granularity objects: queue, job and task. They all need to maintain some information:
(1) Queue maintenance information
@ queuename: the name of the queue
@ ulmin: the minimum amount of resources available for each user (all users are the same), which needs to be specified by the user in the configuration file
@ capacitypercent: to calculate the resource proportion, and
@ numjobsbyuser: the workload of each user in the configuration file to track the workload submitted by each user, And limit the quantity
attributes of map or rec task in the queue:
@ capacity: the actual amount of computing resources, which changes dynamically with the number of slots in the tasktracker (users may be adding or recing machine nodes), The size is: capacitypercent * mapclustercapacity / 100
@ numrunningtasks: the number of running tasks
@ numslotsoccupied: the total number of slots occupied by running tasks. Note that in the capacity Scheler, running tasks and slots do not necessarily correspond one by one, and each task can obtain multiple slots, mainly because the scheling supports memory resource scheling, A task may require the amount of memory contained in multiple slots
@ numslotsoccupiedbyuser: the total number of slots occupied by each user's job is used to limit the amount of resources used by users< br />(2) Job maintenance information
priority: job priority, divided into five levels, from large to small in order: very_ HIGH，HIGH，NORMAL，LOW，VERY_ LOW;
nummaptasks / numrecetasks: the total number of map / rec tasks of the job
runningmaptasks / runningmaptasks: the number of map / rec tasks that the job is running
finishedmaptasks / finishedrecetasks: the number of map / rec tasks that the job has completed
...
(3) Task maintenance information
task start time, current status, etc.
3.2 Computing power scheling algorithm
when there is an idle slot on a tasktracker, the scheler selects a queue, a job in the selected queue, and a task in the selected job in turn, and assigns the slot to the task. The following describes the strategy used to select queue, job and task:
(1) Select queue: sort all the queues according to the resource utilization (Num slots occupied / capacity) from small to large, and process them in turn until a suitable job is found< br />2 Select job: in the current queue, all jobs are sorted according to the job submission time and job priority (assuming that priority scheling function is enabled, which is not supported by default and needs to be enabled in the configuration file). Scheling considers each job in turn, Select a job that meets two conditions: [1] the user where the job is located does not reach the upper limit of resource utilization [2] the node where the tasktracker is located has enough memory for the task of the job< br />3 Select a task. Like most schelers, consider the locality and resource usage of the task That is: call the obtainnewmaptask() / obtainnewrecetask() method in jobinprogress)
in summary, the pseudo code of fair scheler is:
/ / capacitytasks cheler:trackTracker When a free slot appears, find the appropriate task for the slot

List & lt; Task> assignTasks(TaskTrackerStatus taskTracker) {

sortQueuesByResourcesUsesage(queues);< br />
for queue:queues {

4. The theory is like this, specific CPU

5. 1. It's very big. Taking CNN as an example, training needs a lot of data and iteration, which requires high computing power. So Google has dist belief, the network has minwa, either CPU cluster or GPU cluster, the computing power is not enough to play. It takes ten days and a half months to verify the algorithm on Imagenet. I don't know much about it. But I heard about lay.

6. Capacity Scheler supports the following features:
(1) Computing power guarantee. Support multiple queues, a job can be submitted to a queue. Each queue is configured with a certain proportion of computing resources, and all jobs submitted to the queue share the resources in the queue< br />(2) Flexibility. Idle resources will be allocated to those queues that do not reach the upper limit of resource usage. When a queue that does not reach the upper limit of resource needs resources, once there are idle resources, they will be allocated< br />(3) Priority is supported. Queue supports job priority scheling (FIFO by default)
(4) Multiple leases. Considering a variety of constraints to prevent a single job, user or queue from monopolizing the resources in the queue or cluster< br />(5) Resource based scheling. It supports resource intensive jobs, allows jobs to use more resources than the default value, and can accommodate jobs with different resource requirements. However, only memory resource scheling is currently supported< br />3. Analysis of computing power scheler algorithm
3.1 The variables involved
in capacity, there are three kinds of granularity objects: queue, job and task. They all need to maintain some information:
(1) Information maintained by queue

7. On the whole, there are rewards for task segmentation, operation and combination, but the focus of cooperation and processing is different
supercomputing emphasizes high parallel computing capability. Most of the application devices are supercomputers, such as Tianhe-1, which is the high parallel processing architecture of Infiniband to realize bus level collaboration. GPU with stronger computing capability is generally used instead of CPU
cluster computing and distributed computing are relative to the device deployment structure. Compared with supercomputing, this kind of computing has lower requirements for parallel processing and response of computing. What needs to be realized is collaboration in the network environment, and the effect of implementation is affected by the network environment
grid computing is the intermediate proct of cluster computing, distributed computing and supercomputing. In the case that the original cluster computing and distributed computing can not meet the needs, and supercomputing is too difficult to achieve, we want to increase the network bandwidth to achieve the results close to supercomputing through cluster computing and distributed computing, The bandwidth between national grid nodes is T-level, so we can imagine the demand for basic resources
cloud computing is closer to application resource integration. On the premise of coordinating resource integration applications, the parallel processing requirements for application processing are lower, which is only a loosely coupled way. However, it emphasizes the process of task decomposition, processing and combination, so as to make full use of existing resources.

9. Tanjud from the United States summarizes the differences between the three: cloud computing is the development of parallel computing, distributed computing and grid computing, or the commercial realization of these computer science concepts. Cloud computing is the result of the hybrid evolution of virtualization, utility computing, IAAs, PAAS and SaaS. In general, cloud computing can be regarded as a commercial evolution of grid computing. Cloud storage is a storage service provided by some companies. Users store data directly in the company's cluster, such as network disk. Cloud security is a technology to prevent the server providing cloud services from being attacked and the data from being leaked.