Paper Summary. Gaia: Geo-Distributed Auto Learning Approaching Lan Speeds
This newspaper appeared inwards NSDI'17 as well as is authored past times Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B. Gibbons, as well as Onur Mutlu.
Google's Federated Learning likewise considered similar motivations, as well as start to cut back WAN communication. It worked equally follows: 1) smartphones are sent the model past times the master/datacenter parameter-server, 2) smartphones compute an updated model based on their local information over some number of iterations, 3) the updated models are sent from the smartphones to the datacenter parameter-server, 4) the datacenter parameter-server aggregates these models (by averaging) to prepare the novel global model, as well as 5) Repeat.
The Gaia newspaper does non advert Federated Learning paper, because they are probable submitted unopen to the same time. There are many parallels betwixt Gaia's approach as well as that of Federated Learning. Both are based on the parameter-server model, as well as both prescribe updating the model parameters inwards a relaxed/stale/approximate synchronous parallel fashion: several iterations are run in-situ earlier updating the "master" parameter-server. The departure is for Federated Learning at that topographic point is a "master" parameter-server inwards the datacenter, whereas Gaia takes a peer-to-peer approach where each datacenter has a parameter-server, as well as updating the "master" datacenter agency synchronizing the parameter-servers across the datacenters.
ASP builds heavily on the Stale Synchronous Parallelism (SSP) thought for parameter-server ML systems. While SSP bounds how stale (i.e., old) a parameter tin be, ASP bounds how inaccurate a parameter tin be, inwards comparing to the most up-to-date value.
ASP allows the ML programmer to specify the component division as well as the threshold to create upward one's hear the significance of updates for each ML algorithm. The significance threshold has 2 parts: a difficult as well as a soft threshold. The purpose of the difficult threshold is to guarantee ML algorithm convergence, spell that of soft threshold is to utilisation underutilized WAN bandwidth to speed upward convergence. In other words, the soft threshold provides an opportunistic synchronization threshold.
Figure four shows the overview of Gaia. In Gaia, each information centre has some worker machines as well as parameter servers. Each worker machine industrial plant on a shard of the input information stored inwards its information centre to accomplish information parallelism. The parameter servers inwards each information centre collectively keep a version of the global model copy, as well as each parameter server handles a shard of this global model copy. A worker machine alone READs as well as UPDATEs the global model re-create inwards its information center. To cut back the communication overhead over WANs, ASP is used betwixt parameter-servers across dissimilar information centers. Below are the three components of ASP.
The significance filter. ASP takes 2 inputs from user: (1) a significance component division as well as (2) an initial significance threshold. A parameter server aggregates updates from the local worker machines as well as shares the aggregated updates amongst other datacenters when the aggregate becomes significant. To facilitate convergence to the optimal point, ASP automatically reduces the significance threshold over time: if the original threshold is v, so the threshold at iteration t of the ML algorithm is $\frac{v}{\sqrt{t}}$.
ASP selective barrier. When a parameter-server receives the pregnant updates at a charge per unit of measurement that is higher than the WAN bandwidth tin support, instead of sending updates (which volition have got a long time), it kickoff sends a brusk command message to other datacenters. The receiver of this ASP selective barrier message blocks its local workers from reading the specified parameters until it receives the pregnant updates from the sender of the barrier.
Mirror clock. This provides a terminal security internet implementing SSP across datacenters. When each parameter server receives all the updates from its local worker machines at the destination of a clock (e.g., an iteration), it reports its clock to the servers that are inwards accuse of the same parameters inwards the other information centers. When a server detects its clock is ahead of the slowest server, it blocks until the slowest mirror server catches up.
The experiments, running across xi Amazon EC2 global regions as well as on a cluster that emulates EC2 WAN bandwidth, compare Gaia against the Baseline that uses BSP (Bulk Synchronous Parallelism) across all datacenters as well as within a LAN.
What are some examples of advanced significance functions? ML users tin define advanced significance functions to move used amongst Gaia, but this is non explored/explained much inwards the paper. This may move a difficult affair to attain fifty-fifty for advanced users.
Even though it is easier to ameliorate bandwidth than latency, the newspaper focuses on the challenge imposed past times the express WAN bandwidth rather than the WAN latency. While the destination metric for evaluation is completion fourth dimension of training, the newspaper does non investigate the number of network latency. How would the evaluations await if the improvements are investigated inwards correlation to latency rather than throughput limitations? (I guess nosotros tin have got a oil thought on this, if nosotros knew how much the barrier command message was used.)
Motivation
This newspaper proposes a framework to distribute an ML organisation across multiple datacenters, as well as prepare models at the same datacenter where the information is generated. This is useful because it avoids the postulate to movement big information over wide-area networks (WANs), which tin move tedious (WAN bandwidth is near 15x less than LAN bandwidth), costly (AWS does non accuse for within datacenter communication but charges for WAN communication), as well as likewise prone to privacy or ownership concerns.Google's Federated Learning likewise considered similar motivations, as well as start to cut back WAN communication. It worked equally follows: 1) smartphones are sent the model past times the master/datacenter parameter-server, 2) smartphones compute an updated model based on their local information over some number of iterations, 3) the updated models are sent from the smartphones to the datacenter parameter-server, 4) the datacenter parameter-server aggregates these models (by averaging) to prepare the novel global model, as well as 5) Repeat.
The Gaia newspaper does non advert Federated Learning paper, because they are probable submitted unopen to the same time. There are many parallels betwixt Gaia's approach as well as that of Federated Learning. Both are based on the parameter-server model, as well as both prescribe updating the model parameters inwards a relaxed/stale/approximate synchronous parallel fashion: several iterations are run in-situ earlier updating the "master" parameter-server. The departure is for Federated Learning at that topographic point is a "master" parameter-server inwards the datacenter, whereas Gaia takes a peer-to-peer approach where each datacenter has a parameter-server, as well as updating the "master" datacenter agency synchronizing the parameter-servers across the datacenters.
Approximate Synchronous Parallelism (ASP) idea
Gaia's Approximate Synchronous Parallelism (ASP) thought tries to eliminate insignificant communication betwixt information centers spell all the same guaranteeing the correctness of ML algorithms. ASP is motivated past times the observation that the vast bulk of updates to the global ML model parameters from each worker are insignificant, e.g., to a greater extent than than 95% of the updates may attain less than a 1% alter to the parameter value. With ASP, these insignificant updates to the same parameter within a information centre are aggregated (and so non communicated to other information centers) until the aggregated updates are pregnant enough.ASP builds heavily on the Stale Synchronous Parallelism (SSP) thought for parameter-server ML systems. While SSP bounds how stale (i.e., old) a parameter tin be, ASP bounds how inaccurate a parameter tin be, inwards comparing to the most up-to-date value.
ASP allows the ML programmer to specify the component division as well as the threshold to create upward one's hear the significance of updates for each ML algorithm. The significance threshold has 2 parts: a difficult as well as a soft threshold. The purpose of the difficult threshold is to guarantee ML algorithm convergence, spell that of soft threshold is to utilisation underutilized WAN bandwidth to speed upward convergence. In other words, the soft threshold provides an opportunistic synchronization threshold.
Architecture
The Gaia architecture is simple: It prescribes adding a layer of indirection to parameter-server model to draw of piece of occupation organisation human relationship for multiple datacenter deployments.
The significance filter. ASP takes 2 inputs from user: (1) a significance component division as well as (2) an initial significance threshold. A parameter server aggregates updates from the local worker machines as well as shares the aggregated updates amongst other datacenters when the aggregate becomes significant. To facilitate convergence to the optimal point, ASP automatically reduces the significance threshold over time: if the original threshold is v, so the threshold at iteration t of the ML algorithm is $\frac{v}{\sqrt{t}}$.
ASP selective barrier. When a parameter-server receives the pregnant updates at a charge per unit of measurement that is higher than the WAN bandwidth tin support, instead of sending updates (which volition have got a long time), it kickoff sends a brusk command message to other datacenters. The receiver of this ASP selective barrier message blocks its local workers from reading the specified parameters until it receives the pregnant updates from the sender of the barrier.
Mirror clock. This provides a terminal security internet implementing SSP across datacenters. When each parameter server receives all the updates from its local worker machines at the destination of a clock (e.g., an iteration), it reports its clock to the servers that are inwards accuse of the same parameters inwards the other information centers. When a server detects its clock is ahead of the slowest server, it blocks until the slowest mirror server catches up.
Evaluation
The newspaper evaluates Gaia amongst three pop ML applications. Matrix Factorization (MF) is a technique unremarkably used inwards recommender systems. Topic Modeling (TM) is an unsupervised method for discovering hidden semantic structures (topics) inwards an unstructured collection of documents, each consisting of a handbag (multi-set) of words. Image Classification (IC) is a chore to course of didactics images into categories, as well as uses deep learning as well as convolutional neural networks (CNNs). All applications utilisation SGD-based optimization.
The experiments, running across xi Amazon EC2 global regions as well as on a cluster that emulates EC2 WAN bandwidth, compare Gaia against the Baseline that uses BSP (Bulk Synchronous Parallelism) across all datacenters as well as within a LAN.
Questions
Is this full general enough? The introduction says this should apply for SGD based ML algorithms. But are at that topographic point hidden/implicit assumptions?What are some examples of advanced significance functions? ML users tin define advanced significance functions to move used amongst Gaia, but this is non explored/explained much inwards the paper. This may move a difficult affair to attain fifty-fifty for advanced users.
Even though it is easier to ameliorate bandwidth than latency, the newspaper focuses on the challenge imposed past times the express WAN bandwidth rather than the WAN latency. While the destination metric for evaluation is completion fourth dimension of training, the newspaper does non investigate the number of network latency. How would the evaluations await if the improvements are investigated inwards correlation to latency rather than throughput limitations? (I guess nosotros tin have got a oil thought on this, if nosotros knew how much the barrier command message was used.)
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