Wall Clock Vector Free Download UPD

In case of clock skews, when there are no concurrent updates and even when using ALL consistency level, it still might be the case that client has updated value and received ACK from all servers. The actual value however was not updated since provided timestamp was older than existing value at this cell (due to clock skews). Such behaviour violates causal consistency, which AFAIK R+W>N was supposed to provide?

It seems to me that using logical clocks (lamport/vector clocks) to pick newest value and falling back to using actual timestamps (or other strategy that can provided by client) only when concurrent update was detected using read repair. Seems like a better solution and AFAIK this is more or less the approach that dynamo uses, right?

Wall Clock Vector Free Download

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Logical clocks (vector) help to detect concurrent updates, but it won't help to actually decide how to resolve the conflict. E.g. if there are two updates to the same key, vector will detect them, but there is no way to decide which one to use.

Since Cassandra does not return conflicting versions (by design) and does not merge them, they need a way to decide which record to use. They decided to use Last Update Wins strategy. One of options for this strategy is to use wall clock to decide.

As per the CAP theorem, in case of network partitioning, strongly consistent system will have a downtime. We know, logical clocks are strongly consistent, so in case of partitioning they will have a downtime.

In a practical sense, when you implement a logical clock, you implement using one of the quorum based algorithm, which becomes unavailable to the side of network partition, which has lesser number of nodes. So during partitioning, in your example, with a logical clock, either A or B will take writes and the other node will not have access to the logical clock, becoming incapable of serving writes.

Casandra went with 3, but also provides server side default of 2 to simplify clients that don't need logical clock. How you can generate logical time on the client side that is same size integer as a clock time (in millis) is a separate (solved) problem.

On a single machine, all we need to know about is the absolute or wall clock time: suppose we perform a write to key k with timestamp t1 and then perform another write to k with timestamp t2. Since t2 > t1, the second write must have been newer than the first write, and therefore the database can safely overwrite the original value.

In a distributed system, this assumption does not hold. The problem is clock skew, i.e., different clocks tend to run at different rates, so we cannot assume that time t on node a happened before time t + 1 on node b. The most practical techniques that help with synchronizing clocks, like NTP, still do not guarantee that every clock in a distributed system is synchronized at all times. So, without special hardware like GPS units and atomic clocks, just using wall clock timestamps is not enough.

Dynamo truncates vector clocks (oldest first) when they grow too large. If Dynamo ends up deleting older vector clocks that are required to reconcile an object's state, Dynamo would not be able to achieve eventual consistency. Dynamo's authors note that this is a potential problem but do not specify how this may be addressed. They do mention that this problem has not yet surfaced in any of their production systems.

Instead of vector clocks, Dynamo also offers ways to resolve the conflicts automatically on the server-side. Dynamo (and Apache Cassandra) often uses a simple conflict resolution policy: last-write-wins (LWW), based on the wall-clock timestamp. LWW can easily end up losing data. For example, if two conflicting writes happen simultaneously, it is equivalent to flipping a coin on which write to throw away.

clock is not recommended. To return the current date and time as a datetime value, use datetime instead. For more information on updating your code, see Version History or Replace Discouraged Instances of Serial Date Numbers and Date Strings.

To time the duration of an event, use the timeit or tic and toc functions instead of clock and etime. The clock function is based on the system time, which can be adjusted periodically by the operating system, and thus might not be reliable in time comparison operations.

There are no plans to remove clock. However, the datetime function is recommended instead. The datetime data type provides flexible date and time formats, storage out to nanosecond precision, and properties to account for time zones and daylight saving time.

Time is the most valuable thing a person can have. It is what determines your schedule for the day. Though it is important to know the time, it is also good to design great digital clockfaces to make clocks look more attractive and fun to look at.

Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To alleviate this problem, several scalar quantization techniques have been developed to compress the gradients. But these techniques could perform poorly when used together with decentralized aggregation protocols like ring all-reduce (RAR), mainly due to their inability to directly aggregate compressed gradients. In this paper, we empirically demonstrate the strong linear correlations between CNN gradients, and propose a gradient vector quantization technique, named GradiVeQ, to exploit these correlations through principal component analysis (PCA) for substantial gradient dimension reduction. GradiVeQ enables direct aggregation of compressed gradients, hence allows us to build a distributed learning system that parallelizes GradiVeQ gradient compression and RAR communications. Extensive experiments on popular CNNs demonstrate that applying GradiVeQ slashes the wall-clock gradient aggregation time of the original RAR by more than 5X without noticeable accuracy loss, and reduces the end-to-end training time by almost 50%. The results also show that GradiVeQ is compatible with scalar quantization techniques such as QSGD (Quantized SGD), and achieves a much higher speed-up gain under the same compression ratio.

N2 - Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To alleviate this problem, several scalar quantization techniques have been developed to compress the gradients. But these techniques could perform poorly when used together with decentralized aggregation protocols like ring all-reduce (RAR), mainly due to their inability to directly aggregate compressed gradients. In this paper, we empirically demonstrate the strong linear correlations between CNN gradients, and propose a gradient vector quantization technique, named GradiVeQ, to exploit these correlations through principal component analysis (PCA) for substantial gradient dimension reduction. GradiVeQ enables direct aggregation of compressed gradients, hence allows us to build a distributed learning system that parallelizes GradiVeQ gradient compression and RAR communications. Extensive experiments on popular CNNs demonstrate that applying GradiVeQ slashes the wall-clock gradient aggregation time of the original RAR by more than 5X without noticeable accuracy loss, and reduces the end-to-end training time by almost 50%. The results also show that GradiVeQ is compatible with scalar quantization techniques such as QSGD (Quantized SGD), and achieves a much higher speed-up gain under the same compression ratio.

AB - Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To alleviate this problem, several scalar quantization techniques have been developed to compress the gradients. But these techniques could perform poorly when used together with decentralized aggregation protocols like ring all-reduce (RAR), mainly due to their inability to directly aggregate compressed gradients. In this paper, we empirically demonstrate the strong linear correlations between CNN gradients, and propose a gradient vector quantization technique, named GradiVeQ, to exploit these correlations through principal component analysis (PCA) for substantial gradient dimension reduction. GradiVeQ enables direct aggregation of compressed gradients, hence allows us to build a distributed learning system that parallelizes GradiVeQ gradient compression and RAR communications. Extensive experiments on popular CNNs demonstrate that applying GradiVeQ slashes the wall-clock gradient aggregation time of the original RAR by more than 5X without noticeable accuracy loss, and reduces the end-to-end training time by almost 50%. The results also show that GradiVeQ is compatible with scalar quantization techniques such as QSGD (Quantized SGD), and achieves a much higher speed-up gain under the same compression ratio. 2351a5e196