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Today we are extending and generalizing the set-it-and-forget-it model that we pioneered in Spot Fleet with EC2 Fleet, a new building block that gives you the ability to create fleets that are composed of a combination of EC2 On-Demand, Reserved, and Spot Instances with a single API call.
不過目前有些服務還沒整,主要是跟 auto scaling 有關的部份,這部份應該是一次上一大包:
We plan to connect EC2 Fleet and EC2 Auto Scaling groups. This will let you create a single fleet that mixed instance types and Spot, Reserved and On-Demand, while also taking advantage of EC2 Auto Scaling features such as health checks and lifecycle hooks. This integration will also bring EC2 Fleet functionality to services such as Amazon ECS, Amazon EKS, and AWS Batch that build on and make use of EC2 Auto Scaling for fleet management.
Ideally we want to have the ARM performing at or above 50% of the Xeon performance per core. This would make sure we have no performance regressions, and net performance gain, since the ARM CPUs have double the core count as our current 2 socket setup.
In this case, however, I was disappointed to discover an almost 4X slowdown.
Not one to despair, I figured out that applying the same optimizations I did for Intel would be trivial. Surely the NEON instructions map neatly to the SSE instructions I used before?
With the new implementation Centriq outperforms the Xeon at batch reduction for every number of workers. We usually run Polish with four workers, for which Centriq is now 1.3 times faster while also 6.5 times more power efficient.
這篇在提醒之後在 ARM 上寫最佳化時,不要只從 SSE porting 到 NEON,要多看一下有沒有其他指令集是有幫助的...
New S3 One Zone-IA Storage Class – This new storage class is 20% less expensive than the existing Standard-IA storage class. It is designed to be used to store data that does not need the extra level of protection provided by geographic redundancy.
General Purpose SSD (gp2) volumes offer cost-effective storage that is ideal for a broad range of workloads. These volumes deliver single-digit millisecond latencies and the ability to burst to 3,000 IOPS for extended periods of time. Between a minimum of 100 IOPS (at 33.33 GiB and below) and a maximum of 10,000 IOPS (at 3,334 GiB and above), baseline performance scales linearly at 3 IOPS per GiB of volume size. AWS designs gp2 volumes to deliver the provisioned performance 99% of the time. A gp2 volume can range in size from 1 GiB to 16 TiB.
After publishing our material, Amazon engineers pointed that we should try GP2 volumes with the size allocated to provide 10000 IOPS. If we allocated volumes with size 3.3 TiB or more, we should achieve 10000 IOPS.
It turns out that the TPU is even faster on the LSTM model (21402 examples/s): ~12.9 times faster than a P100 (1658 examples/s) and ~7.7 times faster than a V100 (2778 examples/s)!