A Reliable Effective Terascale Linear Learning System

10/19/2011 ∙ by Alekh Agarwal, et al. ∙ Microsoft 0

We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, The number of features here refers to the number of non-zero entries in the data matrix. billions of training examples and millions of parameters in an hour using a cluster of 1000 machines. Individually none of the component techniques are new, but the careful synthesis required to obtain an efficient implementation is. The result is, up to our knowledge, the most scalable and efficient linear learning system reported in the literature (as of 2011 when our experiments were conducted). We describe and thoroughly evaluate the components of the system, showing the importance of the various design choices.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

Code Repositories

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

Distributed machine learning is a research area that has seen a growing body of literature in recent years. Much work focuses on problems of the form

(1)

where

is the feature vector of the

-th example, is the label, is the linear predictor,

is a loss function and

is a regularizer. Most distributed methods for optimizing the objective (1) exploit its natural decomposability over examples, partitioning the examples over different nodes in a distributed environment such as a cluster.

Perhaps the simplest strategy when the number of examples is too large for a given learning algorithm is to reduce the dataset size by subsampling. However, this strategy only works if the problem is simple enough or the number of parameters is very small. The setting of interest here is when a large number of examples is really needed to learn a good model. Distributed algorithms are a natural choice for such scenarios.

It might be argued that even for these large problems, it is more desirable to explore multicore solutions developed for single machines with large amounts of fast storage and memory, rather than a fully distributed algorithm which brings additional complexities due to the need for communication over a network. Yet, we claim that there are natural reasons for studying distributed machine learning on a cluster. In many industry-scale applications, the datasets themselves are collected and stored in a decentralized fashion over a cluster, typical examples being logs of user clicks or search queries. When the data storage is distributed, it is much more desirable to also process it in a distributed fashion to avoid the bottleneck of data transfer to a single powerful server. Second, it is often relatively easy to get access to a distributed computing platform such as Amazon EC2, as opposed to procuring a sufficiently powerful server. Finally, the largest problem solvable by a single machine will always be constrained by the rate at which the hardware improves, which has been steadily dwarfed by the rate at which our data sizes have been increasing over the past decade. Overall, we think that there are several very strong reasons to explore the questions of large-scale learning in cluster environments.

Previous literature on cluster learning is broad. Several authors (Mangasarian, 1995; McDonald et al., 2010; Zinkevich et al., 2010) have studied approaches that first solve the learning problem independently on each machine using the portion of the data stored on that machine, and then average the independent local solutions to obtain the global solution. Duchi et al. (2012) propose gossip-style message passing algorithms extending the existing literature on distributed convex optimization (Bertsekas and Tsitsiklis, 1989). Langford et al. (2009) analyze a delayed version of distributed online learning. Dekel et al. (2012) consider mini-batch versions of distributed online algorithms which are extended to delay-based updates in Agarwal and Duchi (2011). A recent article of Boyd et al. (2011) describes an application of the ADMM technique for distributed learning problems. GraphLab (Low et al., 2010) is a parallel computation framework on graphs. The closest to our work are optimization approaches based on centralized algorithms with parallelized gradient computation (Nash and Sofer, 1989; Teo et al., 2007). To our knowledge, all previous versions of algorithms based on parallelized gradient computation rely on MPI implementations.222http://www.mcs.anl.gov/research/projects/mpi/ Finally, the large-scale learning system Sibyl (currently unpublished, but see the talks Chandra et al., 2010; Canini et al., 2012) implements a distributed boosting approach. It can be used to solve the problems of form (1) at the scales similar to those reported in this paper, but it runs on a proprietary architecture and many implementation details are missing, so doing a fair comparison is currently not possible. We attempt to compare the performance of our algorithm with the published Sibyl performance in Section 3.2.

All of the aforementioned approaches (perhaps with the exception of Sibyl) seem to leave something to be desired empirically when deployed on large clusters. In particular, their learning throughput—measured as the input size divided by the wall-clock running time of the entire learning algorithm—is smaller than the I/O interface of a single machine for almost all parallel learning algorithms (Bekkerman et al., 2011, Part III, page 8). The I/O interface is an upper bound on the speed of the fastest single-machine algorithm since all single-machine algorithms are limited by the network interface in acquiring data. In contrast, we were able to achieve a learning throughput of 500M features/s, which is about a factor of 5 faster than the 1Gb/s network interface of any one node. This learning throughput was achieved on a cluster of 1000 nodes. Each node accessed its local examples 10 times during the course of the algorithm, so the per-node processing speeds were 5M features/s. We discuss our throughput results in more detail in Section 3.2, and contrast them with results reported for Sibyl.

Two difficulties bedevil easy parallel machine learning:

  1. Efficient large-scale parallel learning algorithms must occur on a data-centric computing platform (such as Hadoop) to prevent data transfer overheads. These platforms typically do not support the full generality of MPI operations.

  2. Existing data-centric platforms often lack efficient mechanisms for state synchronization and force both refactoring and rewriting of existing learning algorithms.

We effectively deal with both of these issues. Our system is compatible with MapReduce clusters such as Hadoop (unlike MPI-based systems) and minimal additional programming effort is required to parallelize existing learning algorithms (unlike MapReduce approaches). In essence, an existing implementation of a learning algorithm need only insert a few strategic library calls to switch from learning on one machine to learning on a thousand machines.

One of the key components in our system is a communication infrastructure that efficiently accumulates and broadcasts values across all nodes of a computation. It is functionally similar to MPI AllReduce (hence we use the name), but it takes advantage of and is compatible with Hadoop so that programs are easily moved to data, automatic restarts on failure provide robustness, and speculative execution speeds up completion. Our optimization algorithm is a hybrid online+batch algorithm with rapid convergence and only small synchronization overhead, which makes it a particularly good fit for the distributed environment.

In Section 2 we describe our approach and our communication infrastructure in more detail. The core of the paper is Section 3 where we conduct many experiments evaluating our design choices and comparing our approach with existing algorithms. In Section 4 we provide some theoretical intuition for our design, and contrast our approach with previous work. We conclude with a discussion in Section 5.

2 Computation and Communication Framework

MapReduce (Dean and Ghemawat, 2008) and its open source implementation Hadoop333http://hadoop.apache.org/ have become the overwhelmingly favorite platforms for distributed data processing. However, the abstraction is rather ill-suited for machine learning algorithms as several researchers in the field have observed (Low et al., 2010; Zaharia et al., 2011), because it does not easily allow iterative algorithms, such as typical optimization algorithms used to solve the problem (1).

2.1 Hadoop-compatible AllReduce

AllReduce is a more suitable abstraction for machine learning algorithms. AllReduce is an operation where every node starts with a number and ends up with the sum of the numbers across all the nodes (hence the name). A typical implementation imposes a tree structure on the communicating nodes and proceeds in two phases: numbers are first summed up the tree (the reduce phase) and then broadcast down to all the nodes (the broadcast phase), see Figure 1 for a graphical illustration. When doing summing or averaging of a long vector, such as the weight vector in the optimization (1), the reduce and broadcast operations can be pipelined over the vector entries and hence the latency of going up and down the tree becomes negligible on a typical Hadoop cluster. This is the main optimization we do within the AllReduce architecture. While other (potentially more efficient or simpler) architectures for AllReduce are possible, in our experiments in Section 3 we will see that the time spent in AllReduce operation is negligible compared with the computation time and stalling time while waiting for other nodes. Therefore, we do not attempt to optimize the architecture further.

Figure 1: AllReduce operation. Initially, each node holds its own value. Values are passed up the tree and summed, until the global sum is obtained in the root node (reduce phase). The global sum is then passed back down to all other nodes (broadcast phase). At the end, each node contains the global sum.

For problems of the form (1), AllReduce provides straightforward parallelization of gradient-based optimization algorithms such as gradient descent or L-BFGS—gradients are accumulated locally, and the global gradient is obtained by AllReduce. In general, any statistical query algorithm (Kearns, 1993) can be parallelized with AllReduce with only a handful of additional lines of code. This approach also easily implements averaging parameters of online learning algorithms.

An implementation of AllReduce is available in the MPI package. However, it is not easy to run MPI on top of existing Hadoop clusters (Ye et al., 2009). Moreover, MPI implements little fault tolerance, with the bulk of robustness left to the programmer.

To address the reliability issues better, we developed an implementation of AllReduce that is compatible with Hadoop. Our implementation works as follows. We initialize a spanning tree server on the gateway node to the Hadoop cluster. We then launch a map-only (alternatively reduce-only) job where each mapper processes a subset of the data. Each mapper is supplied with the IP address of the gateway node, to which it connects as the first step. Once all the mappers are launched and connected to the spanning tree server, it creates a (nearly balanced) binary tree on these nodes. Each node is given the IP addresses of its parent and child nodes in the tree, allowing it to establish TCP connections with them. All the nodes are now ready to pass messages up and down the tree. The actual communication between the nodes is all implemented directly using C++ sockets and does not rely on any Hadoop services. Implementation of AllReduce using a single tree is clearly less desirable than MapReduce in terms of reliability, because if any individual node fails, the entire computation fails. To deal with this problem, we use a simple trick described below which makes AllReduce reliable enough to use in practice for computations up to 10K node hours.

It is noteworthy that the idea of using AllReduce for iterative computations has recently gained traction in the Hadoop community. For instance, Knitting Boar is an abstraction on top of YARN—the new scheduler for Hadoop—which is “similar in nature to the AllReduce primitive”.444See https://github.com/jpatanooga/KnittingBoar/wiki/IterativeReduce.

2.2 Proposed Algorithm

Our main algorithm is a hybrid online+batch approach. Pure online and pure batch learning algorithms have some desirable features, on which we build, and some drawbacks, which we overcome. For instance, an attractive feature of online learning algorithms is that they optimize the objective to a rough precision quite fast, in just a handful of passes over the data. The inherent sequential nature of these algorithms, however, makes them tricky to parallelize and we discuss the drawbacks of some of the attempts at doing so in Section 4. Batch learning algorithms such as Newton and quasi-Newton methods (e.g., L-BFGS), on the other hand, are great at optimizing the objective to a high accuracy, once they are in a good neighborhood of the optimal solution. But the algorithms can be quite slow in reaching this good neighborhood. Generalization of these approaches to distributed setups is rather straightforward, only requiring aggregation across nodes after every iteration, as has been noted in previous research (Teo et al., 2007).

We attempt to reap the benefits and avoid the drawbacks of both above approaches through our hybrid method. We start with each node making one online pass over its local data according to adaptive gradient updates (Duchi et al., 2010; McMahan and Streeter, 2010) modified for loss non-linearity (Karampatziakis and Langford, 2011). We notice that each online pass happens completely asynchronously without any communication between the nodes, and we can afford to do so since we are only seeking to get into a good neighborhood of the optimal solution rather than recovering it to a high precision at this first stage. AllReduce is used to average these weights non-uniformly according to locally accumulated gradient squares. Concretely, node maintains a local weight vector and a diagonal matrix based on the gradient squares in the adaptive gradient update rule (see Algorithm 1). We compute the following weighted average over all nodes

(2)

This has the effect of weighting each dimension according to how “confident” each node is in its weight (i.e., more weight is assigned to a given parameter of a given node if that node has seen more examples with the corresponding feature). We note that this averaging can indeed be implemented using AllReduce by two calls to the routine since the matrices are only diagonal.

This solution is used to initialize L-BFGS (Nocedal, 1980) with the standard Jacobi preconditioner, with the expectation that the online stage gives us a good warmstart for L-BFGS. At each iteration, global gradients are obtained by summing up local gradients via AllReduce, while all the other operations can be done locally at each node. The algorithm benefits from the fast initial reduction of error provided by an online algorithm, and rapid convergence in a good neighborhood guaranteed by quasi-Newton algorithms. We again point out that the number of communication operations is relatively small throughout this process.

In addition to hybrid strategy, we also evaluate repeated online learning with averaging using the adaptive updates. In this setting, each node performs an online pass over its data and then we average the weights according to Equation 2. We average the scaling matrices similarly

and use this averaged state to start a new online pass over the data. This strategy is similar to those proposed by McDonald et al. (2010) and Hall et al. (2010) for different online learning algorithms. We will see in the next section that this strategy can be very effective at getting a moderately small test error very fast, but its convergence slows down and it might be too slow at reaching the optimal test error.

All strategies described above share the same processing structure. They carry out several iterations, each of which can be broken into three phases: (1) Pass through the entire local portion of the dataset and accumulate the result as a vector of size  (i.e., the same size as the parameter vector). (2) Carry out AllReduce operation on a vector of size . (3) Do some additional processing and updating of the parameter vector.

The key point to notice is that in typical applications the local dataset will be orders of magnitude larger than the parameter vector, hence the communication after each pass is much more compact than transmitting the entire local dataset. The second point is that each iteration is naturally a MapReduce operation. The main reason that we expect to benefit by AllReduce is because of the iterative nature of these algorithms and the shared state between iterations.

Our implementation is available as part of the open source project Vowpal Wabbit (Langford et al., 2007) and is summarized in Algorithm 2

. It makes use of stochastic gradient descent (Algorithm 

1) for the initial pass.

0:  Invariance update function
             (see Karampatziakis and Langford, 2011)
  
  for all  in training set do
     
     
      for all
  end for
Algorithm 1 Stochastic gradient descent algorithm on a single node using adaptive gradient update (Duchi et al., 2010; McMahan and Streeter, 2010).
0:  Data split across nodes
  for all nodes  do
      = result of stochastic gradient descent on the data of node using Algorithm 1.
     Compute the weighted average as in (2) using AllReduce.
     Start a preconditioned L-BFGS optimization from .
     for  do
         Compute the (local batch) gradient of examples on node .
         Compute using AllReduce.
         Add the regularization part in the gradient.
         Take an L-BFGS step.
     end for
  end for
Algorithm 2 Sketch of the proposed learning architecture

2.3 Speculative Execution

Large clusters of machines are typically busy with many jobs which use the cluster unevenly, resulting in one of a thousand nodes being very slow. To avoid this, Hadoop can speculatively execute a job on identical data, using the first job to finish and killing the other one. In our framework, it can be tricky to handle duplicates once a spanning tree topology is created for AllReduce. For this reason, we delay the initialization of the spanning tree until each node completes the first pass over the data, building the spanning tree on only the speculative execution survivors. The net effect of this speculative execution trick is perhaps another order of magnitude of scalability and reliability in practice. Indeed, we found the system reliable enough for up to 1000 nodes running failure-free for hundreds of trials (of typical length up to 2 hours). This level of fault tolerance highlights the benefits of a Hadoop-compatible implementation of AllReduce. We will show the substantial gains from speculative execution in mitigating the “slow node” problem in the experiments.

3 Experiments

3.1 Datasets

Display Advertising

In online advertising, given a user visiting a publisher page, the problem is to select the best advertisement for that user. A key element in this matching problem is the click-through rate (CTR) estimation: what is the probability that a given ad will be clicked on, given some context (user, page visited)? Indeed, in a cost-per-click (CPC) campaign, the advertiser only pays when the ad gets clicked, so even modest improvements in predictive accuracy directly affect revenue.

Training data contains user visits, which either resulted in a click on the ad (positive examples with ), or did not result in a click (negative examples with

). We estimate the click probabilities by logistic regression with

regularization. The regularization coefficient is chosen from a small set to obtain the best test performance. The user visit is represented by binary indicator features encoding the user, page, ad, as well as conjunctions of these features. Some of the features include identifiers of the ad, advertiser, publisher and visited page. These features are hashed (Weinberger et al., 2009) and each training example ends up being represented as a sparse binary vector of dimension  with around 125 non-zero elements. Let us illustrate the construction of a conjunction feature with an example. Imagine that an ad from etrade was placed on finance.yahoo.com. Let be a 24 bit hash of the string “publisher=finance.yahoo.com and advertiser=etrade”. Then the (publisher, advertiser) conjunction is encoded by setting to 1 the -th entry of the feature vector for that example.

Since the data is unbalanced (low CTR) and because of the large number of examples, we subsample the negative examples resulting in a class ratio of about 2 negatives for 1 positive, and use a large test set drawn from days later than the training set. There are 2.3B examples in the training set. More characteristics of this dataset and modeling details can be found in (Chapelle et al., 2013)

Splice Site Recognition

The problem consists of recognizing a human acceptor splice site (Sonnenburg and Franc, 2010). We consider this learning task because this is, as far as we know, the largest public dataset for which subsampling is not an effective learning strategy. Sonnenburg et al. (2007) introduced the weighted degree kernel to learn over DNA sequences. They also proposed an SVM training algorithm for that kernel for which learning over 10M sequences took 24 days. Sonnenburg and Franc (2010) proposed an improved training algorithm, in which the weight vector—in the feature space induced by the kernel—is learned, but the feature vectors are never explicitly computed. This resulted in a faster training: 3 days with 50M sequences.

We solve this problem by -regularized logistic regression. Again, the regularization coefficient is chosen from a small set to optimize test set performance. We follow the same experimental protocol as in Sonnenburg and Franc (2010): we use the same training and test sets of respectively 50M and 4.6M samples. We also consider the same kernel of degree and hash size . The feature space induced by this kernel has dimensionality 11,725,480. The number of non-zero features per sequence is about 3,300. Unlike Sonnenburg and Franc (2010), we explicitly compute the feature space representation of the examples, yielding about 3TB of data. This explicit representation is a disadvantage we impose on our method to simplify implementation.

3.2 Results

Effect of Subsampling

The easiest way to deal with a very large training set is to reduce it by subsampling as discussed in the introduction. Sometimes similar test errors can be achieved with smaller training sets and there is no need for large-scale learning. For splice site recognition, Table 2 of Sonnenburg and Franc (2010) shows that smaller training sets do hurt the area under the precision/recall curve on the test set.

For display advertising, we subsampled the data at 1% and 10%. The results in Table 1 show that there is a noticeable drop in accuracy after subsampling. Note that even if the drop does not appear large at a first sight, it can cause a substantial loss of revenue. Thus, for both datasets, the entire training dataset is needed to achieve optimal performances.

The three metrics reported in Table 1 are area under the ROC curve (auROC), area under the precision/recall curve (auPRC) and negative log-likelihood (NLL). Since auPRC is the most sensitive metric, we report test results using that metric in the rest of the paper. This is also the metric used in Sonnenburg and Franc (2010).

1% 10% 100%
auROC 0.8178 0.8301 0.8344
auPRC 0.4505 0.4753 0.4856
NLL 0.2654 0.2582 0.2554
Table 1: Test performance on the display advertising problem as a function of the subsampling rate, according to three metrics: area under ROC curve (auROC), area under precision/recall curve (auPRC), and negative log likelihood (NLL).

Running Time

We ran 5 iterations of L-BFGS on the splice site data with 1000 nodes. On each node, we recorded for every iteration the time spent in AllReduce and the computing time—defined as the time not spent in AllReduce. The time spent in AllReduce can further be divided into stall time—waiting for the other nodes to finish their computation—and communication time. The communication time can be estimated by taking the minimum value of the AllReduce times across nodes.

The distribution of the computing times is of particular interest because the speed of our algorithm depends on the slowest node. Statistics are shown in Table 2

. It appears that computing times are concentrated around the median, but there are a few outliers. Without speculative execution, one single node was about 10 times slower than the other nodes; this has the catastrophic consequence of slowing down the entire process by a factor 10. The table shows that the use of speculative execution successfully mitigates this issue.

5% 50% 95% Max Comm. time
Without spec. execution 29 34 60 758 26
With spec. execution 29 33 49 63 10
Table 2:

Distribution of computing times (in seconds) over 1000 nodes with and without speculative execution. First three columns are quantiles. Times are average per iteration (excluding the first one) for the splice site recognition problem.

We now study the running time as a function of the number of nodes. For the display advertising problem, we varied the number of nodes from 10 to 100 and computed the speed-up factor relative to the run with 10 nodes. In each case, we measured the amount of time needed to get to a fixed test error. Since there can be significant variations from one run to the other—mostly because of the cluster utilization—each run was repeated 10 times. Results are reported in Figure 2. We note that speculative execution was not turned on in this experiment, and we expect better speed-ups with speculative execution. In particular, we expect that the main reason for the departure from the ideal speed-up curve is the “slow node” problem (as opposed to the aspects of the AllReduce communication implementation), which is highlighted also in the next experiment.

Figure 2: Speed-up for obtaining a fixed test error, on the display advertising problem, relative to the run with 10 nodes, as a function of the number of nodes. The dashed line corresponds to the ideal speed-up, the solid line is the average speed-up over 10 repetitions, and the bars indicate maximum and minimum values.
Nodes 100 200 500 1000
Comm time / pass 5 12 9 16
Median comp time / pass 167 105 43 34
Max comp time / pass 462 271 172 95
Wall clock time 3677 2120 938 813
Table 3: Computing times to obtain a fixed test error on the splice site recognition data, using different numbers of nodes. The first 3 rows are averages per iteration (excluding the first pass over the data).
Figure 3: Effect of initializing the L-BFGS optimization by an average solution from online runs on individual nodes. Suboptimality is the difference between the objective value on the training data and the optimal value obtained by running the algorithm to convergence.

Table 3 shows the running times for attaining a fixed test error as a function of the number of nodes on the splice site recognition problem. Unlike Figure 2, these timing experiments have not been repeated and thus there is a relatively large uncertainty in their expected values. It can be seen from Tables 2 and 3 that even with as many as 1000 nodes, communication is not the bottleneck. One of the main challenges instead is the “slow node” issue. This is mitigated to some degree by speculative execution, but as the number of nodes increases, so does the likelihood of hitting slow nodes.

Large Experiment and Comparison with Sibyl

We also experimented with an 8 times larger version of the display advertising data (16B examples). Using 1000 nodes and 10 passes over the data, the training took only 70 minutes.555As mentioned before, there can be substantial variations in timing between different runs; this one was done when the cluster was not too occupied. Since each example is described by 125 non-zero features, the average processing speed was

The overall learning throughput was

We briefly compare this with a result reported for the distributed boosting system Sibyl for a run on 970 cores (Canini et al., 2012, slide 24). The run was done over 129.1B examples, with 54.61 non-zero features per example. The reported processing speed was 2.3M features/core/s (which is a factor of two slower than our achieved processing speed). The reported number of iterations was 10–50, which would lead to the final learning throughput in the range 45–223 M features/s, i.e., the result appears to be slower by a factor of 2–10.

Online and Batch Learning

We now investigate the speed of convergence of three different learning strategies: batch, online and hybrid. We are interested in how fast the algorithms minimize the training objective as well as the test error.

Figure 3 compares how fast the two learning strategies—batch with and without an initial online pass— optimize the training objective. It plots the optimality gap, defined as the difference between the current objective function and the optimal one (i.e., the minimum value of the objective (1)), as a function of the number iterations. From this figure we can see that the initial online pass results in a saving of about 10–15 iterations.

Splice site recognition Display advertising   

Figure 4: Test auPRC for 4 different learning strategies. Note that the online and hybrid curves overlap during the warmstart phase (of either 1 or 5 online passes).

Figure 4 shows the test auPRC on both datasets as a function of the number of iterations for 4 different strategies: only online learning, only L-BFGS learning, and 2 hybrid methods consisting of 1 or 5 passes of online learning followed by L-BFGS optimization. L-BFGS with one online pass appears to be the most effective strategy.

For the splice site recognition problem, an initial online pass and 14 L-BFGS iterations yield an auPRC of 0.581, which is just a bit higher than results of Sonnenburg and Franc (2010). This was achieved in 1960 seconds using 500 machines, resulting in a 68 speed-up factor (132,581 seconds on a single machine reported in Table 2 of Sonnenburg and Franc (2010)). This seems rather poor compared with the ideal 500 speed-up factor, but recall that we used explicit feature representation which creates a significant overhead.

3.3 Comparison with Previous Approaches

AllReduce vs. MapReduce

The standard way of using MapReduce for iterative machine learning algorithms is the following (Chu et al., 2007): every iteration is a MapReduce job where the mappers compute some local statistics (such as a gradient) and the reducers sum them up. This is ineffective because each iteration has large overheads (job scheduling, data transfer, data parsing, etc.). We have an internal implementation of such a MapReduce algorithm. We updated this code to use AllReduce instead and compared both versions of the code in Table 4. This table confirms that Hadoop MapReduce has substantial overheads since the training time is not much affected by the dataset size. The speed-up factor of AllReduce over Hadoop’s MapReduce can become extremely large for smaller datasets, and remains noticeable even for the largest datasets. It is also noteworthy that all algorithms described in Chu et al. (2007) can be parallelized with AllReduce, plus further algorithms such as parameter averaging approaches.

Full size 10% sample
MapReduce 1690 1322
AllReduce 670 59
Table 4: Average training time per iteration of an internal logistic regression implementation using either MapReduce or AllReduce for gradients aggregation. The dataset is the display advertising one and a subset of it.

Overcomplete Average

We implemented oversampled stochastic gradient with final averaging (Zinkevich et al., 2010), and compared its performance to our algorithm. We used stochastic gradient descent with the learning rate in the -th iteration as

We tuned and on a small subset of the dataset.

In Figure 5, we see that the oversampled SGD is competitive with our approach on the display advertising data, but its convergence is much slower on splice site data.

Splice site recognition Display advertising   

Figure 5: Test auPRC for different learning strategies as a function of the effective number of passes over data. In L-BFGS, it corresponds to iterations of the optimization. In overcomplete SGD with averaging (Zinkevich et al.), it corresponds to the replication coefficient.

Parallel Online Mini-batch

Dekel et al. (2012) propose to perform online convex optimization using stochastic gradients accumulated in small mini-batches across all nodes. We implemented a version of their algorithm using AllReduce. They suggest global minibatch sizes of no more than . On nodes, each node accumulates gradients from examples, then an AllReduce operation is carried out, yielding the mini-batch gradient, and each node performs a stochastic gradient update with the learning rate of the form

We tuned and on a smaller dataset. In Figure 5, we report the results on splice site dataset, using 500 nodes, and mini-batch size . Twenty passes over the data thus corresponded to 10k updates. Due to the overwhelming communication overhead associated with the updates, the overall running time was 40 hours. In contrast, L-BFGS took less than an hour to finish 20 passes while obtaining much superior performance. The difference in the running time between 1h and 40h is solely due to communication. Thus, in this instance, we can conservatively conclude that the communication overhead of 10k mini-batch updates is 39 hours.

We should point out that it is definitely possible that the mini-batched SGD would reach similar accuracy with much smaller mini-batch sizes (for 10k updates theory suggests we should use mini-batch sizes of at most 10k), however, the 39 hour communication overhead would remain. Using larger mini-batches, we do expect that the time to reach 20 passes over data would be smaller (roughly proportional to the number of mini-batch updates), but according to theory (as well as our preliminary experiments on smaller subsets of splice site data), we would have inferior accuracy. Because of the prohibitive running time, we were not able to tune and evaluate this algorithm on display advertising dataset.

4 Communication and Computation Complexity

The two key performance characteristics of any distributed algorithm are its communication and computation complexity. The aim of this section is to discuss the complexity of our approach and to compare it with previous solutions. We hope to clarify the reasons underlying our design choices and explain the scalability of our system. We start with a discussion of computational considerations.

4.1 Computational Complexity of the Hybrid Approach

In this section, we explain the convergence properties of the hybrid approach and compare it with other optimization strategies. In order to have a clean discussion, we make some simplifying assumptions. We consider the case of only one online pass at each node. Furthermore, we restrict ourselves to the case of uniform averaging of weights. Similar analysis does extend to the non-uniform weighting scheme that we use, but the details are technical and provide no additional intuition. Before we embark on any details, it should be clear that the hybrid approach is always convergent, owing to the convergence of L-BFGS. All the online learning step initially does is to provide a good warmstart to L-BFGS. This section aims to provide theoretical intuition why the gains of such a warmstart can be substantial in certain problem regimes.

Let be the regularized loss function. We analyze a scaled version of the objective (1):

We assume that the cluster is comprised of nodes, with a total of data examples distributed uniformly at random across these nodes. Let us denote the local objective function at each node as :

where is the set of examples at node . Note that the global objective is the average of the local objectives. We observe that owing to our random data split, we are guaranteed that

for each , where the expectation is taken over the distribution from which our examples are drawn. In order to discuss the convergence properties, we need to make a couple of standard assumptions regarding the functions . First, we assume that the functions are differentiable, with Lipschitz-continuous gradients. We also assume that each is strongly convex, at least in a local neighborhood around the optimum. We note that these assumptions are unavoidable for the convergence of quasi-Newton methods such as L-BFGS.

To understand how many passes over the data are needed for the hybrid approach to minimize to a precision , we first analyze the online learning pass at each node. In this pass, we compute a weight vector by performing steps of stochastic gradient descent or some variant thereof (Duchi et al., 2010; Karampatziakis and Langford, 2011). Since we performed only one pass at each node, the resulting at each node approximately minimizes to a precision (for the methods we use, we expect ). Let us now denote the uniform average . For this approach, a direct application of Jensen’s inequality yields

(3)

Furthermore, standard sample complexity arguments (see, e.g., Bartlett and Mendelson, 2002; Devroye et al., 1996) allow us to bound the function value for an arbitrary as

(4)

Let be the minimizer of the training loss function . Then we can combine the above inequalities as

where the first inequality follows by (4), the second by (3), and the fourth by (4). For the remainder of the discussion, we denote the overall suboptimality of relative to by .

Switching over to the L-BFGS phase, we assume that we are in the linear convergence regime of L-BFGS (Liu and Nocedal, 1989). We denote the contraction factor by , so that the number of additional L-BFGS passes over data needed to minimize to a precision is at most

Compared to initializing L-BFGS without any warmstart, our hybrid strategy amounts to overall savings of

passes over data. In typical applications, we expect to ensure that computation amortize the cost of communication. As a result, the improvement due to the warmstart can be quite substantial just like we observed in our experiments. Furthermore, this part of our argument is in no way specific to the use of L-BFGS as the batch optimization algorithm. Similar reasoning holds for any reasonable (quasi)-Newton method.

We could also consider the alternative choice of just using parallel online learning without ever switching to a batch optimization method. The theoretical results in this area, however, are relatively harder to compare with the hybrid approach. For general online learning algorithms, previous works study just one local pass of online learning followed by averaging (McDonald et al., 2010), which typically cannot guarantee an error smaller than

in our earlier notation. The repeated averaging approach, discussed and analyze for the specific case of perceptron algorithm in earlier work 

(McDonald et al., 2010), works well in our experiments on the computational advertising task but does not have easily available convergence rates beyond the special case of separable data and the perceptron algorithm. Nevertheless, one appeal of the hybrid approach is that it is guaranteed to be competitive with such online approaches, by the mere virtue of the first online phase.

Overall, we see that the hybrid approach will generally be competitive with purely online or batch approaches in terms of the computational complexity. As a final point, we discuss two extreme regimes where it can and does offer substantial gains. The first regime is when the data has a significant noise level. In such a scenario, the level of optimization accuracy desired is typically not too large (intuitive statistical arguments show no reduction in generalization error for ). Setting for a clean comparison, we observe that the total number of passes for the hybrid method is at most

as opposed to for just pure batch optimization. When , this shows that the online warmstart can cut down the number of passes almost by a factor of 2. We do note that in such high noise regimes, pure online approaches can often succeed, as we observed with our advertising data.

The second extreme is when our data is essentially noiseless, so that the desired accuracy is extremely small. In this case, the relative impact of the online warmstart can be less pronounced (it is certainly strictly better still) over an arbitrary initialization of L-BFGS. However, as we saw on our splice site recognition data, on this extreme, the online learning methods will typically struggle since they are usually quite effective in fitting the data to moderate but not high accuracies (as evident from their or convergence rates). Overall, we find that even on these two extremes, the hybrid approach is competitive with the better of its two components.

4.2 Communication Cost Comparison with Previous Approaches

In the previous section we discussed the computational complexity of several techniques with an identical communication pattern: communication of the entire weight vector once per pass. In this section we contrast our approach with techniques that use other communication patterns. We focus mainly on communication cost since the computational cost is typically the same as for our algorithm, or the communication dominates the computation.

Since modern network switches are quite good at isolating communicating nodes, the most relevant communication cost is the maximum (over nodes) of the communication cost of a single node.

Several variables (some of them recalled from the previous section) are important:

  1. the number of nodes.

  2. the total number of examples across all nodes.

  3. the number of nonzero features per example.

  4. the parameter dimension.

  5. the number of passes over the examples.

In the large-scale applications that are subject of this paper, we typically have , where both and are large (see Section 3.1).

The way that data is dispersed across a cluster is relevant in much of this discussion since an algorithm not using the starting format must pay the communication cost of redistributing the data. We assume the data is distributed across the nodes uniformly according to an example partition, as is common.

Algorithm Per-node communication cost
Bundle method (Teo et al., 2007)
Online with averaging (McDonald et al., 2010; Hall et al., 2010)
Parallel online (Hsu et al., 2011)
Overcomplete online with averaging (Zinkevich et al., 2010)
Distr. minibatch (dense) (Dekel et al., 2012; Agarwal and Duchi, 2011)
Distr. minibatch (sparse) (Dekel et al., 2012; Agarwal and Duchi, 2011)
Hybrid online+batch
Table 5: Communication cost of various learning algorithms. Here is the number of examples, is the number of nonzero features per example, is the number of dimensions, is the number of times the algorithm examines each example, and is the minibatch size (in minibatch algorithms).

The per-node communication cost of the hybrid algorithm is where is typically about to maximize test accuracy in our experiments. Note that the minimum possible communication cost is if we save the model on a single machine. There is no communication involved in getting data to workers based on the data format assumed above. An important point here is that every node has a communication cost functionally smaller than the size of the dataset, because there is no dependence on .

Similar to our approach, Teo et al. (2007) propose a parallel batch optimization algorithm (specifically, a bundle method) using the MPI implementation of AllReduce. This approach arrives at an accurate solution with communication per node. Our approach improves over this in several respects. First, as Figure 4 demonstrates, we obtain a substantial boost thanks to our warmstarting strategy, hence in practice we expect . The second distinction is in the AllReduce implementation. Our implementation is well aligned with Hadoop and takes advantage of speculative execution to mitigate the slow node problem. On the other hand, MPI assumes full control over the cluster, which needs to be carefully aligned with Hadoop’s MapReduce scheduling decisions, and by itself, MPI does not provide robustness to slow nodes.

Batch learning can also be implemented using MapReduce on a Hadoop cluster (Chu et al., 2007), for example in the Mahout project.666http://mahout.apache.org/ Elsewhere it has been noted that MapReduce is not well suited for iterative machine learning algorithms (Low et al., 2010; Zaharia et al., 2011). Evidence of this is provided by the Mahout project itself, as their implementation of logistic regression is not parallelized. Indeed, we observe substantial speed-ups from a straightforward substitution of MapReduce by AllReduce on Hadoop. It is also notably easier to program with AllReduce, as code does not require refactoring.

The remaining approaches are based on online convex optimization. McDonald et al. (2010) and Hall et al. (2010) study the approach when each node runs an online learning algorithm on its examples and the results from the individual nodes are averaged. This simple method is empirically rather effective at creating a decent solution. The communication cost is structurally similar to our algorithm when passes are done. However, as we saw empirically in Figure 4 and also briefly argued theoretically in Section 4.1, .

Similarly to these, Zinkevich et al. (2010) create an overcomplete partition of the data and carry out separate online optimization on each node followed by global averaging. Our experiments show that this algorithm can have competitive convergence (e.g., on display advertising data), but on more difficult optimization problems it can be much slower than the hybrid algorithm we use here (e.g., on splice site recognition data). This approach also involves deep replication of the data—for example, it may require having 1/4 of all the examples on each of 100 nodes. This is generally undesirable with large datasets. The per-node communication cost is where is the level of replication and is the number of nodes. Here, the first term comes from the data transfer required for creating the overcomplete partition whereas the second term comes from parameter averaging. Since is often a constant near ( was observed by Zinkevich et al., 2010, and the theory predicts only a constant factor improvement), this implies the communication cost is , the size of the dataset.

Other authors have looked into online mini-batch optimization (Dekel et al., 2012; Agarwal and Duchi, 2011). The key problem here is the communication cost. The per-node communication cost is where is the minibatch size (number of examples per minibatch summed across all nodes), is the number of passes over the data, is the number of minibatch updates per pass and is the number of parameters. Theory suggests , implying communication costs of . While for small minibatch sizes can be quite small (plausibly even smaller than ), when is sufficiently large, this communication cost is prohibitively large. This is the reason for the slow performance of mini-batched optimization that we observed in our experiments. Reworking these algorithms with sparse parameter updates, the communication cost per update becomes yielding an overall communication cost of , which is still several multiples of the dataset size. Empirically, it has also been noted that after optimizing learning rate parameters, the optimal minibatch size is often  (Hsu et al., 2011).

Another category of algorithms is those which use online learning with a feature based partition of examples (Hsu et al., 2011; Dean et al., 2012). The advantage of this class of algorithms is that they can scale to a very large number of parameters, more than can be fit in the memory of a single machine. Several families of algorithms have been tested in (Hsu et al., 2011)

including delayed updates, minibatch, second-order minibatch, independent learning, and backpropagation. The per-node communication costs differ substantially here. Typical communication costs are

where the first term is due to shuffling from an example-based format, and the second term is for the run of the actual algorithm. The complexity of our approach is superior to this strategy since .

5 Discussion

We have shown that a new architecture for parallel learning based on a Hadoop-compatible implementation of AllReduce can yield a combination of accurate prediction and short training time in an easy programming style. The hybrid algorithm we employ allows us to benefit from the rapid initial optimization of online algorithms and the high precision of batch algorithms where the last percent of performance really matters. Our experiments reveal that each component of our system is critical in driving the performance benefits we obtain. Specifically, Table 4 and Figure 3 show the performance gains resulting from our use of AllReduce and the warmstart of the L-BFGS algorithm. The effectiveness of our overall system, as compared to the previous approaches, is confirmed in Figure 5. Two issues we do not discuss in this paper are the overheads of data loading and node scheduling within Hadoop. These issues can indeed affect the performance, but we found that they typically get amortized since they are one-time overheads in the AllReduce approach as opposed to per-iteration overheads in MapReduce. Nonetheless, improvements in the scheduling algorithms can further improve the overall performance of our system.

Our paper carefully considers various design choices affecting the communication and computation speeds of a large-scale linear learning system, drawing from and building upon the available techniques in the literature. The resulting system enables the training of linear predictors on datasets of size unmatched in previous published works.

References

  • Agarwal and Duchi (2011) A. Agarwal and J. Duchi. Distributed delayed stochastic optimization. In Advances in Neural Information Processing Systems 24. 2011.
  • Bartlett and Mendelson (2002) P. L. Bartlett and S. Mendelson. Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3:463–482, 2002.
  • Bekkerman et al. (2011) R. Bekkerman, M. Bilenko, and J. Langford. A tutorial on scaling up machine learning. Technical report, KDD, 2011. URL http://hunch.net/~large_scale_survey/.
  • Bertsekas and Tsitsiklis (1989) D. P. Bertsekas and J. N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Prentice-Hall, Inc., 1989.
  • Boyd et al. (2011) S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3:1–122, 2011.
  • Canini et al. (2012) K. Canini, T. Chandra, E. Ie, J. McFadden, K. Goldman, M. Gunter, J. Harmsen, K. LeFevre, D. Lepikhin, T. L. Llinares, I. Mukherjee, F. Pereira, J. Redstone, T. Shaked, and Y. Singer. Sibyl: A system for large scale supervised machine learning. In MLSS Santa Cruz, 2012. URL http://users.soe.ucsc.edu/~niejiazhong/slides/chandra.pdf. A short presentation.
  • Chandra et al. (2010) T. Chandra, E. Ie, K. Goldman, T. L. Llinares, J. McFadden, F. Pereira, J. Redstone, T. Shaked, and Y. Singer. Sibyl: a system for large scale machine learning. In LADIS 2010: The 4th ACM SIGOPS/SIGACT Workshop on Large Scale Distributed Systems and Middleware, 2010. URL http://www.magicbroom.info/Papers/Ladis10.pdf. A keynote talk.
  • Chapelle et al. (2013) O. Chapelle, E. Manavoglu, and R. Rosales. Simple and scalable response prediction for display advertising. Transactions on Intelligent Systems and Technology, 2013. In press.
  • Chu et al. (2007) C.T. Chu, S.K. Kim, Y.A. Lin, Y.Y. Yu, G. Bradski, A.Y. Ng, and K. Olukotun. Map-reduce for machine learning on multicore. In Advances in Neural Information Processing Systems 19, volume 19, page 281, 2007.
  • Dean and Ghemawat (2008) J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51:107–113, 2008.
  • Dean et al. (2012) J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, M. A. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Ng. Large scale distributed deep networks. In Advances in Neural Information Processing Systems 25, pages 1232–1240. 2012.
  • Dekel et al. (2012) O. Dekel, R. Gilad-Bachrach, O. Shamir, and L. Xiao. Optimal distributed online prediction using mini-batches. Journal of Machine Learning Research, 13:165–202, 2012.
  • Devroye et al. (1996) L. Devroye, L. Györfi, and G. Lugosi.

    A Probabilistic Theory of Pattern Recognition

    .
    Springer, 1996.
  • Duchi et al. (2010) J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121–2159, 2010.
  • Duchi et al. (2012) J.C. Duchi, A. Agarwal, and M.J. Wainwright. Dual averaging for distributed optimization: Convergence analysis and network scaling. Automatic Control, IEEE Transactions on, 57(3):592–606, 2012.
  • Hall et al. (2010) K. Hall, S. Gilpin, and G. Mann. Mapreduce/bigtable for distributed optimization. In Workshop on Learning on Cores, Clusters, and Clouds, 2010.
  • Hsu et al. (2011) D. Hsu, N. Karampatziakis, J. Langford, and A. Smola. Parallel online learning. In Scaling Up Machine Learning, 2011.
  • Karampatziakis and Langford (2011) N. Karampatziakis and J. Langford. Online importance weight aware updates. In

    Uncertainty in Artificial Intelligence

    , 2011.
  • Kearns (1993) M. Kearns. Efficient noise-tolerant learning from statistical queries. In

    Proceedings of the Twenty-Fifth Annual ACM Symposium on the Theory of Computing

    , 1993.
  • Langford et al. (2007) J. Langford, L. Li, and A. Strehl. Vowpal wabbit open source project. Technical report, Yahoo!, 2007.
  • Langford et al. (2009) J. Langford, A. Smola, and M. Zinkevich. Slow learners are fast. In Advances in Neural Information Processing Systems 22, 2009.
  • Liu and Nocedal (1989) D. C. Liu and J. Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical Programming, 45:503–528, 1989.
  • Low et al. (2010) Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new framework for parallel machine learning. In Uncertainty in Artificial Intelligence, 2010.
  • Mangasarian (1995) O. L. Mangasarian. Parallel gradient distribution in unconstrained optimization. SIAM Journal on Optimization, 33:1916–1925, 1995.
  • McDonald et al. (2010) R. McDonald, K. Hall, and G. Mann. Distributed training strategies for the structured perceptron. In North American Chapter of the Association for Computational Linguistics (NAACL), 2010.
  • McMahan and Streeter (2010) H. B. McMahan and M. Streeter. Adaptive bound optimization for online convex optimization. In

    Proceedings of the Twenty Third Annual Conference on Computational Learning Theory

    , 2010.
  • Nash and Sofer (1989) S. G. Nash and A. Sofer. Block truncated-newton methods for parallel optimization. Mathematical Programming, 45:529–546, 1989.
  • Nocedal (1980) J. Nocedal. Updating quasi-Newton matrices with limited storage. Math. Comp., 35(151):773–782, 1980.
  • Sonnenburg and Franc (2010) S. Sonnenburg and V. Franc. COFFIN: a computational framework for linear SVMs. In International Conference on Machine Learning, 2010.
  • Sonnenburg et al. (2007) S. Sonnenburg, G. Rätsch, and K. Rieck. Large scale learning with string kernels. In Large Scale Kernel Machines, pages 73–103. 2007.
  • Teo et al. (2007) C. Teo, Q. Le, A. Smola, and SVN Vishwanathan. A scalable modular convex solver for regularized risk minimization. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2007.
  • Weinberger et al. (2009) K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg. Feature hashing for large scale multitask learning. In International Conference on Machine Learning, 2009.
  • Ye et al. (2009) J. Ye, J.-H. Chow, J. Chen, and Z. Zheng.

    Stochastic gradient boosted distributed decision trees.

    In Proceeding of the 18th ACM conference on Information and knowledge management, 2009.
  • Zaharia et al. (2011) M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. Franklin, S. Shenker, and I. Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. Technical Report UCB/EECS-2011-82, EECS Department, University of California, Berkeley, 2011.
  • Zinkevich et al. (2010) M. Zinkevich, M. Weimer, A. Smola, and L. Li. Parallelized stochastic gradient descent. In Advances in Neural Information Processing Systems 23. 2010.