I am learning solidity/ethereum and I came across this situation:
I have a mapping(address => unit) that keeps track of how much every address is paying my contract, and at some point, I have to compute how much % of the total pool has one user contributed with. (for example, if the total pool is 100 ethers and user contributed 10 ethers, he has contributed with 10% of the total pool).
In order to do so, I need to have access to the total pool. My first instinct was to have a variable totalPool which will keep track of the total value, therefore every time an address is paying the contract, totalPool += msg.value; However, while learning about the EVM, I kept reading how expensive it is to operate on the storage.
My question is, what is cheaper in terms of gas, to keep track of the total pool and operate on memory every time an address pays the contract, or to compute the total pool every time when I need to find out the ratio contribution?
From what I understand of your use case, your first instinct is probably the simplest and the best solution unless you have an easy way to compute the total pool. You have to keep in mind that in solidity, it is impossible to loop over the elements of a mapping to sum them up. So unless it is possible to calculate the size of your pool using other variables that would be stored anyway, the total pool variable is most likely the best way to keep track of the pool size.
I highly recommend that you test as many implementations as you can come up with. Both the ethers.js and web3.js libraries have functions that allows you to test how much gas should be required to execute a transaction.
Related
I'm interested in the conceptual topic of creating rights managements systems on the the Ethereum block chain with digital assets represented by an NFT.
I am just reading up on how to write programs that run on Etherium but I have some very basic questions just to get to started.
I read that NFT are created on the Ethereum blockchain. I don't really understand if that is the same block chain on which the currency Ether is maintained? Seems like the ledger will become impossibly large huge if both the every currency transaction and every digital asset and copy thereof that migrates to Ethereum is stored in one single giant ledger and that each miner on the chain has to download the entire ledger to one single machine in order to validate transactions? Have I got big misunderstanding there? I know there is talk about "sharding" in the future, but it seems like that isn't coming very soon.
Cost of running a smart contract on the blockchain? Assuming that the we are talking about the same block chain, from what I can see the price of "Gas" is quite high. I'm reading that the price of ETH transfer from one party to another is 21,000 Gwei, about $0.03 today. Just trying to understand the basics, how much does it cost to create a NFT? And roughly how much does it cosst to execute a simple function on the blockchain (without loops). Let say the equivalent of 5 statement function which takes a few simple params, reads a few blocks, doesn't write to the block chain but just performs some simple math and a few if statements and returns a string? Does that also cost, like, more than penny? Is the conversion to ETH2 switch from proof of work to proof of stake going to bring those costs down by orders of magnitude?
Any good resources or reference on how to write programs which create and manipulate NFTS on Etherium? Most of what I have seen in the bookstores seem to cover financial transactions with Ether.
Yes, it's the same blockchain.
You can see in the stats that full node (stores current state) currently takes about 400 GB and archive node (stores current and historical states as well) takes about 6.6 TB.
My observation is that most web apps using blockchain data don't verify and trust a third-party service running a node (such as Infura). And I believe that most end users or businesses who want/need to verify, usually have the capacity to store 400+ GB and are able to scale.
But if this amount of data is okay or "impossibly large huge", I'll leave that to your decision. :)
Deployment of a token smart contract usually costs between 500k to 3M gas. My estimate is that most token contracts with basic features that were compiled with an optimizer, cost around 1M gas to deploy. With current prices of ~200 Gwei/gas and $1800/ETH, that's about $350. But I remember just few months ago the average gas prices were ~20 and ETH cost $500, so that would be around $10. So yea, the cost of deploying a contract is very volatile.
Simple function that performs validations and transformations in memory is going to cost the base 21k + few hundred gas. (Working with memory data is cheap gas-wise, accessing the storage is much more expensive.) So in current prices around $7, few months ago it could have been $0.25.
As for the question, whether ETH2.0 is going to bring lower gas price: My opinion is that L2 (which should be released earlier than PoS) is going to have some effect on the price since it allows for sidechain transactions (similar to Lightning network on Bitcoin). But this is a development forum, so I'm not not going to dive deeper into price speculations.
I recommend OpenZeppelin docs where they cover their opensource implementations of ERC standards (including ERC-721 NFTs) or googling the topic you're interested in and read articles that catch your eye (at least that's my current approach).
And if you're new to Solidity in general, I recommend at least few chapters from CryptoZombies tutorial. In my opinion, the first few chapters are great and you'll learn a lot, but then the quality slowly fades.
I have N number of agents/users accessing a single wireless channel and at each time, only one agent can access the channel and receive a reward.
Each user has a buffer that can store B number of packets and I assume it as infinite buffer.
Each user n gets observation from the environment if the packet in time slot t was successful or failure (collision). If more than one users access the channel, they get penalty.
This feedback from the channel is same for all the users since we only have one channel. The reward is - B_n (negative of the number of packets in buffer). Each user wants to maximize its own reward and try to empty the buffer.
Packets arrive at each users following a poisson process with average $\lambda$ packets per time slot.
Each user has a history of previous 10 time slots that it uses as an input to the DQN to output the probability of taking action A_n: stay silent or transmit. The history is (A_n, F, B_n)
Each user is unaware of the action and buffer status of other users.
I am trying to model my problem with multiagent reinforcement learning and so far I have tried it with DQN but results are more or less like a random scheme. It could be that users don't have much contextual information in order to learn the behaviour of other users? Or can there be any other reason?
I would like to know how can I model my environment since the state (in RL sense) is static, the environment doesn't changes. The only thing that changes is each users history at each time slot. So I am not sure if its a partially observable MDP or should it be modelled as multiagent single-arm bandit problem which I don't know is correct or not.
The second concern is that I have tried DQN but it has not worked and I would like to know if such problem can be used with tabular Q-learning? I have not seen multiagent works in which anyone has used QL. Any insights might be helpful.
Your problem can be modeled as a Decentralized POMDP (see a overview here).
Summarizing this approach, you consider a multi-agent system where each agent model his own policy, and then you try to build a joint policy through these individual ones. Of course that, the complexity grows as the number of agents, states and actions increases,so for that you have several approaches mainly based in heuristics to prune branches of this joint policy tree that are not "good" in comparison with others. A very know example using this approach is exactly about routing packages where is possible define a discrete action/space.
But be aware that even for tiny system, the complexity becomes often infeasible!
Is it faster to cudaHostRegister a moving address range (window) or copy into a fixed Registered address range?
cudaHostRegister pins host memory (makes pageable memory unpageable) to accelerate transfer from host to device memory. One can register a range of host memory, then transfer, then register another range, etc. Or, alternatively, one can register a single range of host memory, copy into that range and transfer, then repeat the process with a second data set without re-registering the address range.
The host registration process (which involves CUDA interacting with the operating system) is costly in terms of time spent. As a rule of thumb, it may cost as much in time as the time spent transferring the data (once), effectively doubling the time spent transferring the data, the first time the buffer is used.
Therefore, from a performance perspective alone, registering a buffer and using it once is rarely a significant performance win (you may still wish to do it, however, to enable other things like concurrency).
If a buffer will be used repeatedly, pinning (i.e. registering) the buffer is often a performance win, where the benefit increases with the level of reuse of the buffer, compared to pinning a single contiguous region and using it once. However this doesn't take into consideration the cost of copying the data to and from the pinned buffer, if that will be necessary.
The benefits here, if any, of pinnning, will vary depending on your specific usage pattern, and exactly what form of usage you will be comparing it to. However, to repeat, pinning a buffer and using it exactly once is rarely a significant performance win, compared to using non-pinned memory in the transfer.
Can any one clarify whether (or, not) an increase in mining pool in Ethereum network with decrease the average block generation time? (For example, if another pool like ethermine join the network today and start mining). Since all the pools are competing with each other, I am getting confused
No, block generation times are driven by the current difficulty for solving the the algorithm used in the Proof of Work model. Only when a solution is found, is the block accepted to the chain and the difficulty determines how long it will take to find that solution. This difficulty automatically adjusts to speed up or slow down block generation times.
From the mining section of the Ethereum wiki:
The proof of work algorithm used is called Ethash (a modified version of Dagger-Hashimoto) involves finding a nonce input to the algorithm so that the result is below a certain threshold depending on the difficulty. The point in PoW algorithms is that there is no better strategy to find such a nonce than enumerating the possibilities while verification of a solution is trivial and cheap. If outputs have a uniform distribution, then we can guarantee that on average the time needed to find a nonce depends on the difficulty threshold, making it possible to control the time of finding a new block just by manipulating difficulty.
The difficulty dynamically adjusts so that on average one block is produced by the entire network every 12 seconds.
(Note that the current block generation time is closer to 15 seconds. You can find the block generation times on Etherscan)
Is there a formula someplace which can be used to determine the minimum number of segments / bytes which need to be transfered across a TCP connection to determine it's bandwidth and which takes into account Slow Start and Congestion Avoidance? I'm aware of the pathrate tool, but I want if possible something a bit simpler that I can incorporate in an app to get a descent ballpark figure. One example of usage would be downloading some data from a webserver in order to determine the optimum number of threads for downloading a bunch of small files automatically. This is related to a previous question I posted: TCP, HTTP and the Multi-Threading Sweet Spot
You can fire up scholar.google.com and search for "TCP chirp". However, that requires hires timers, and if you don't write a kernel tcp congestion control algorithm, you'd have to reimplement TCP in userspace. And that by itself will probably not give good results (general purpose OS are not very good at realtime hires timer related stuff, runnning in userspace).
In theory, using TCP chirp you need as few as 4-5 segments (typically, you'd get better resolution with a longer train of segments) to determine the "optimal" bandwidth.
In any case, since you can not know which path is used (ie. satellite link or tv broadcast in the forward direction), you may need a considerable amount of data (10+ MB, perhaps even 1GB) to get a decent measurement over arbitrary paths. (Satellites can have many dozend MB/s bandwidth, but also latencies in the 1000-3000 ms range; and TCP takes a couple round-trip times to open up cwnd (I'd say around 10 RTTs before a measurement should be started)...
I do not think that there is a fixed number of bytes required to be sent to determine the bandwidth. This number can depend on network type and speed.
Bandwidth is a measure of some resource transferred over a time interval. To get real data you need to measure it. Here are some hints how to do that