throttled | Workaround for Intel throttling issues in Linux
kandi X-RAY | throttled Summary
kandi X-RAY | throttled Summary
This tool was originally developed to fix Linux CPU throttling issues affecting Lenovo T480 / T480s / X1C6 as described here. The CPU package power limit (PL1/2) is forced to a value of 44 W (29 W on battery) and the temperature trip point to 95 'C (85 'C on battery) by overriding default values in MSR and MCHBAR every 5 seconds (30 on battery) to block the Embedded Controller from resetting these values to default. On systems where the EC doesn't reset the values (ex: ASUS Zenbook UX430UNR), the power limit can be altered by using the official intel_rapl driver (see Static fix for more information).
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Top functions reviewed by kandi - BETA
- Power a thread
- Returns the write time of the config file
- Get CPU information
- Calculate the maximum Icc max value
- Check if the system is on power
- Update undervoltages
- Calculate the reg values for the power source
- Writes a 32 - bit value to the given offset
- Reads a 32 - bit value from the given offset
- Set Hwp energy preference
- Calculate register values for the power source
- Monitor the power plane
- Check that the CPU is supported
- Check if the system is on battery
- Validate the kernel configuration
- Calculate the ICC maximum value for each plane
- Manage undervoltages
- Test if mr is supported
- Try to unlock the MSR process
- Set HWP energy
- Get CPU platform info
throttled Key Features
throttled Examples and Code Snippets
Lines of Code : 169License : Permissive (Apache-2.0)
Copyprivate:
// ----------------------------------------------------------------------
// Types
// ----------------------------------------------------------------------
enum class ThrottleState {
THROTTLED,
NOT_THROTTLED
};
F32 Test private:
// ----------------------------------------------------------------------
// Types
// ----------------------------------------------------------------------
enum class ThrottleState {
THROTTLED,
NOT_THROTTLED
};
F32 Test module Ref {
@ Component for receiving and performing a math operation
queued component MathReceiver {
# ----------------------------------------------------------------------
# General ports
# -------------------------------------- public void setIsThrottled (boolean isThrottled) throws Exception {
this.isThrottled = isThrottled;
} public boolean getIsThrottled () throws Exception {
return this.isThrottled;
} Community Discussions
Trending Discussions on throttled
QUESTION
I develop a highly loaded application that reads data from DynamoDB on-demand table. Let's say it constantly performs around 500 reads per second.
From time to time I need to upload a large dataset into the database (100 million records). I use python, spark and audienceproject/spark-dynamodb. I set throughput=40k and use BatchWriteItem() for data writing.
In the beginning, I observe some write throttled requests and write capacity is only 4k but then upscaling takes place, and write capacity goes up.
Questions:
- Does intensive writing affects reading in the case of on-demand tables? Does autoscaling work independently for reading/writing?
- Is it fine to set large throughput for a short period of time? As far as I see the cost is the same in the case of on-demand tables. What are the potential issues?
- I observe some throttled requests but eventually, all the data is successfully uploaded. How can this be explained? I suggest that the client I use has advanced rate-limiting logic and I didn't manage to find a clear answer so far.
ANSWER
Answered 2022-Mar-29 at 15:28That's a lot of questions in one question, you'll get a high level answer.
DynamoDB scales by increasing the number of partitions. Each item is stored on a partition. Each partition can handle:
- up to 3000 Read Capacity Units
- up to 1000 Write Capacity Units
- up to 10 GB of data
As soon as any of these limits is reached, the partition is split into two and the items are redistributed. This happens until there is sufficient capacity available to meet demand. You don't control how that happens, it's a managed service that does this in the background.
The number of partitions only ever grows.
Based on this information we can address your questions:
-
Does intensive writing affects reading in the case of on-demand tables? Does autoscaling work independently for reading/writing?
The scaling mechanism is the same for read and write activity, but the scaling point differs as mentioned above. In an on-demand table AutoScaling is not involved, that's only for tables with provisioned throughput. You shouldn't notice an impact on your reads here.
-
Is it fine to set large throughput for a short period of time? As far as I see the cost is the same in the case of on-demand tables. What are the potential issues?
I assume you set the throughput that spark can use as a budget for writing, it won't have that much of an impact on on-demand tables. It's information, it can use internally to decide how much parallelization is possible.
-
I observe some throttled requests but eventually, all the data is successfully uploaded. How can this be explained? I suggest that the client I use has advanced rate-limiting logic and I didn't manage to find a clear answer so far.
If the client uses BatchWriteItem, it will get a list of items that couldn't be written for each request and can enqueue them again. Exponential backoff may be involved but that is an implementation detail. It's not magic, you just have to keep track of which items you've successfully written and enqueue those that you haven't again until the "to-write" queue is empty.
QUESTION
I'm fairly new to programming (< 3 years exp), so I don't have a great understanding of the subjects in this post. Please bear with me.
My team is developing an integration with a third party system, and one of the third party's endpoints lacks a meaningful way to get a list of entities matching a condition.
We have been fetching these entities by looping over the collection of requests, and adding the results of each awaited call to a list. This works just fine, but getting the entities takes a lot longer than getting entities from other endpoints that lets us get a list of entities by providing a list of ids.
.NET 6.0 introduced Parallel.ForEachAsync(), which lets us execute multiple awaitable tasks asynchronously in parallel.
For example:
...ANSWER
Answered 2021-Dec-09 at 16:18My suggestion is to ditch the Parallel.ForEachAsync approach, and use instead the new Chunk LINQ operator in combination with the Task.WhenAll method. You can launch 100 asynchronous operations every second like this:
QUESTION
I am calling the below function which returns me Promise
ANSWER
Answered 2022-Mar-24 at 01:09The reason why the return type is Promise is that the generic of Typecript expects the type based on the parameters entered. Therefore, the return type of PromiseFunction in res2 is Promise. The UnwrappedReturnType type expects the PromiseFn type return value. At this time, the ApiFn type is an extends of PromiseFn, and the PromiseFn type's return value is Promise, so the UnwrappedReturnType type is any.
Again, the errorHandler generic ApiFn type used as a parameter is the same as PromiseFn((...args: any[]) => Promise) type because there are no parameters expected.
In other words, if you specify the ApiFn generic type, res2 type inference is possible.
QUESTION
I am creating Django middleware for blocking a user when (s)he gets throttled more than 5 times but I am getting ContentNotRenderedError.
ANSWER
Answered 2022-Mar-23 at 18:34If you really want to return a Response instance, you need to set some properties before returning it:
QUESTION
I am trying to find out a way to calculate the maximum and minimum values of a column.
Using the query below, I first calculate the (requests per minute) RPM by using the summary operation and then want to pick max and min values for the RPM column.
I can technically use the take operation after ordering the column (asc or desc) to get either min or max value but it doesn't seem to be computationally efficient. Also, it only provides either max or min value and not both values at the same time.
The final output should be like following table:
...ANSWER
Answered 2022-Mar-10 at 19:04You can use the arg_min() and arg_max() aggregation functions, on top of your already-aggregated counts.
For example:
QUESTION
In gRPC go, how can I know if a client is getting throttled by a server. Is there any event I could listen to to observe this?
In my case, I'm using a simple Unary.
I've used a tcpdump and checked for the frequency of window update event, but I guess there may be a better way to do so.
...ANSWER
Answered 2022-Feb-16 at 18:33Channelz might have what you want: https://github.com/grpc/proposal/blob/master/A14-channelz.md#socket-data
You need to make a server: https://pkg.go.dev/google.golang.org/grpc@v1.44.0/channelz/service#RegisterChannelzServiceToServer, and use a grpc client to read the data (e.g. https://github.com/grpc-ecosystem/grpcdebug)
If I remember correctly, it doesn't send signals when flowcontrol window is depleted. But it prints the window size for debugging purposes.
And note that frequency of window update event doesn't indicate if the client is blocked on flowcontrol. Window updates are exchanges regularly to keep track of the flow control window.
QUESTION
I have azure functions developed in node js. When I create a cloud instance for function app, it gets stuck on deployment process with all the resources OK status. Microsoft.Web/serverfarms returning 429. The error message reads as:
...ANSWER
Answered 2021-Dec-18 at 05:04Status code 429 refers to throttling, there is a possibility that there is some other deployment going on in your subscription that might be causing this throttling behavior. You can try redeploying.
App Service Plans have a 10 per region limit on the Free SKU as documented here.
If you are using the free plan and need more than 10 plans, you would need to either move to a paid plan, or raise a support ticket to see if the limit can be raised (I suspect not on the free plan).
If your not using the free plan then check the other limits in that link, if you don't feel you are hitting any then you would need to raise a support case. Billing cases are free.
QUESTION
All the documentation I found about rx.net throttling does not cover the overload with a parameter Func> throttleDurationSelector. So all I have available are the XML-comments. This suggests, that throttleDurationSelector is called on every new element in the source sequence. The expected return value would be a IObservable. (My understanding) This enables the possibility to change the throttle-delay on every new element. But, this understanding does not match the runtime experience I discover.
ANSWER
Answered 2022-Feb-13 at 09:42We are talking about this overload of the Throttle operator:
QUESTION
I want to throttle function calls which are added as Event Listeners to the window.scroll function by a 3rd party library provided by an external supplied (cant be changed).
I figured out that the library causes some overhead by its scroll event listener, because if I remove the event handler, my page runs much smoother.
As I cannot directly control or change the external JS file, I thought to read the scroll-events attached to the Window and delete / rebind them again, but in a throttled format, as I have already the Underscore.js library in use.
I'm trying to read the Scroll events and than replace the function callback as a throttled version:
...ANSWER
Answered 2022-Feb-09 at 20:18Found a beautiful solution using Underscore.js, proxying the callback functions by a Throttler before adding it as Event Handler:
QUESTION
I have a .net core backend with SignalR and a react frontend. I have a basic hub set up with ConcurrentDictionary to manage connection ids:
...ANSWER
Answered 2022-Jan-31 at 03:26I just tried it on one of my sites and it seems like the way the dev tools performs the throttling disrupts websocket connections to the point that it doesn't seem to work bi-directionally whether it is on slow3g or fast3g simulation. I can reproduce your error on my otherwise working site. My suspicion is the simulator, not your code.
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Install throttled
You can use throttled like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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