Tux2: Distributed Graph Computation For Car Learning
The TUX2 newspaper appeared inward NSDI 17 together with was authored by Wencong Xiao, Beihang University together with Microsoft Research; Jilong Xue, Peking University together with Microsoft Research; Youshan Miao, Microsoft Research; Zhen Li, Beihang University together with Microsoft Research; Cheng Chen together with Ming Wu, Microsoft Research; Wei Li, Beihang University; Lidong Zhou, Microsoft Research.
TUX2 introduces around novel concepts to graph procedure engines to adjust them ameliorate for auto learning (ML) preparation jobs. Before I tin speak nearly the contributions of TUX2, you lot demand to demeanor alongside me equally I explicate how electrical flow graph processing frameworks autumn brusk for ML training.
A topic-modeling algorithm similar Latent Drichlet Allocation (LDA) tin last modeled equally a document-word graph. If a document contains a word, at that topographic point is an border betwixt them; the information on that border are the topics of the discussion inward the document. Even logistic regression tin last modeled equally a sample-feature graph.
2. For ML computation, an iteration of a graph computation mightiness involve multiple rounds of propagation betwixt dissimilar types of vertices, rather than a uncomplicated serial of GAS phases. The touchstone GAS model is unable to limited such computation patterns efficiently.
3. Machine learning frameworks receive got been shown to produce goodness from the Stale Synchronous Parallel (SSP) model, a relaxed consistency model alongside bounded staleness to improve parallelism. The graph processing engines usage Bulk Synchronous Parallel (BSP) model past times default.
1. supports heterogeneity inward the information model,
2. advocates a novel graph model, MEGA (Mini-batch, Exchange, GlobalSync, together with Apply), that allows flexible composition of stages, and
3. supports SSP inward execution scheduling.
Next nosotros speak over the basic pattern elements inward TUX2, together with how the inward a higher house three capabilities are built on them.
Vertex-cut is real useful for implementing the parameter-server model: The original versions of all vertices' information tin last treated equally the distributed global nation stored inward a parameter server. The mirrors are distributed to workers, which likewise has the instant type of vertices together with usage the mirror vertices to iterate on these instant type of vertices.
Wait, the instant type of vertices? Yes, hither nosotros harken dorsum to the bipartite graph model. Recall that nosotros had bipartite graph alongside heterogeneous vertices, alongside around vertices having higher degrees. Those higher flat vertices are original vertices together with held at the server, together with the depression flat vertices are data/training for those original vertices together with they cache the original vertices equally mirror vertices together with prepare on them. And, inward around sense, the partitions of low-order vertex type inward the bipartite graph corresponds to mini-batch.
The newspaper has the next to say on this. In a bipartite graph, TUX2 tin enumerate all edges past times scanning alone vertices of 1 type. The choice of which type to enumerate sometimes has pregnant performance implications. Scanning the vertices alongside mirrors inward a mini-batch tends to Pb to a to a greater extent than efficient synchronization step, because these vertices are placed contiguously inward an array. In contrast, if TUX2 scans vertices without mirrors inward a mini-batch, the mirrors that acquire updated for the other vertex type during the scan volition last scattered together with thus to a greater extent than expensive to locate. TUX2 thence allows users to specify which laid of vertices to enumerate during the computation.
Each sectionalization is managed past times a procedure that logically plays both
Figure three illustrates how TUX2 organizes vertex information for a bipartite graph, using MF on a user-item graph equally an example. Because user vertices receive got much smaller flat inward general, alone especial vertices are dissever past times vertex-cut partitioning. Therefore, a original vertex array inward the server role contains alone especial vertices, together with the worker role alone manages user vertices. This way, at that topographic point are no mirror replicas of user vertices together with no distributed synchronization is needed. In the worker role, the mirrors of especial together with user vertices are stored inward ii assort arrays.
In each partition, TUX2 maintains vertices together with edges inward assort arrays. Edges inward the border array are grouped past times origin vertex. Each vertex has an index giving the offset of its edge-set inward the border array. Each border contains information such equally the id of the sectionalization containing the destination vertex together with the index of that vertex inward the corresponding vertex array. This graph information construction is optimized for traversal together with outperforms vertex indexing using a lookup table. Figure 2 shows how information are partitioned, stored, together with assigned to execution roles inward TUX2.
TUX2 supports SSP inward the mini-batch granularity. It tracks the progress of each mini-batch iteration to enable computation of clocks. A worker considers clock t completed if the corresponding mini-batch is completed on all workers (including synchronizations betwixt masters together with mirrors) together with if the resulting update has been applied to together with reflected inward the state. A worker tin execute a undertaking at clock t alone if it knows that all clocks upwardly to t−s−1 receive got completed, where s is the allowed slack.
MEGA allows users to build an arbitrary sequence of stages. Unlike GAS, which needs to last repeated inward social club (i.e., GAS-GAS-GAS-GAS), inward MEGA you lot tin flexibly mix together with tally (e.g., E-A-E-A-G). For example, inward algorithms such equally MF together with LDA, processing an border involves updating both vertices. This requires ii GAS phases, simply tin last accomplished inward 1 Exchange stage inward META. For LR, the vertex information propagations inward both directions should last followed past times an Apply phase, simply no Scatter phases are necessary; this tin last avoided inward the MEGA model because MEGA allows an arbitrary sequence of stages.
Below are examples of Matrix factorization (MF) together with Latent Dirichlet Allocation (LDA) programmed alongside the META model. (LDA's stage sequence is the same equally MF's.)
The evaluations exhibit that information layout matters greatly inward the performance of ML algorithms. Figure 8 compares the performance of BlockPG, MF, together with LDA alongside ii dissimilar layouts: 1 an array-based graph information layout inward TUX2 together with the other a hash-table-based lay-out oft used inward parameter-server-based systems (but implemented inward TUX2 for comparison). The y-axis is the average running fourth dimension of 1 iteration for BlockPG, together with of 10 iterations for MF together with LDA to exhibit the numbers on a similar scale. These results exhibit that the graph layout improves performance past times upwardly to 2.4× over the hash-table-based layout.
The newspaper likewise includes a comparing alongside Petuum, simply the evaluations receive got several caveats. The evaluations produce non include comparing of convergence/execution time; execution fourth dimension per iteration does non ever create upwardly one's heed the convergence time. The evaluations produce non accept into concern human relationship the partitioning fourth dimension of the graph for TUX2. And finally, around comparisons used early on unpatched version of Petuum MF algorithm whose information placement issues are resolved later.
I similar this paper; it made me inquire together with ponder on many questions, which is good.
I don't shout upwardly TUX2 pushes the nation of the fine art inward ML. ML processing frameworks are already real efficient together with full general alongside the iterative parameter-server computing model, together with they are getting ameliorate together with to a greater extent than fine grained.
On the other hand, I shout upwardly TUX2 is valuable because it showed how the high-level graph computing frameworks tin last adapted to implement the low-level parameter-server approach together with address ML preparation problems to a greater extent than efficiently. This may supply around advantages for problems that are/need to last represented equally graphs, such equally for performing ML preparation on Sparql information stores.
Moreover past times using higher-level primitives, TUX2 provides around rest of programmability. I gauge this may last leveraged farther to accomplish around plug together with play programming of ML for sure shape of programs.
So I respect this to last a conceptually real satisfying newspaper equally it bridges the graph processing model to parameter-server model. I am less sure nearly the practicality part.
2. How does graph processing frameworks compare alongside dataflow frameworks?
There are large differences betwixt dataflow frameworks together with graph processing frameworks. In the dataflow model, there is a symbolic computation graph, the graph nodes correspond mathematical operations, spell the graph edges correspond the information that menstruum betwixt the operations. That is a real dissimilar model than the graph processing model here.
In MEGA, at that topographic point are alone 4 stages, where the Apply stage tin accept inward user defined functions. This is higher-level (and arguably to a greater extent than programming friendly) than a dataflow framework such equally TensorFlow which has many hundreds of predefined operators equally vertices.
3. How does TUX2 apply to Deep Learning (DL)?
The newspaper does non speak nearly whether TUX2 tin apply to DL together with how it tin apply.
It may last possible to brand DL fit the TUX2 model alongside around stretching. Deep neural network (DNN) layers (horizontally or vertically partitioned) could last the high-rank vertices concur inward the servers. And the images are low-ranked vertices concur inward partitions.
But this volition require treating the DNN partions equally a meta-vertex together with schedule executions for each sub-vertes inward the meta-vertex inward 1 cycle. I receive got no clue nearly how to brand backpropagation piece of work hither though.
Moreover, for each image, the ikon may demand to link to entire NN, so the bipartite graph may collapse into a piddling 1 together with piddling data-parallelism. It may last possible to brand the convolutional layers tin last distributed. It may fifty-fifty last possible to insert early on exits together with prepare that way.
So, it may last possible simply it is surely non straightforward. I am non fifty-fifty touching the bailiwick of the performance of such a system.
TUX2 introduces around novel concepts to graph procedure engines to adjust them ameliorate for auto learning (ML) preparation jobs. Before I tin speak nearly the contributions of TUX2, you lot demand to demeanor alongside me equally I explicate how electrical flow graph processing frameworks autumn brusk for ML training.
Background together with motivation
Graph processing engines oft takes a "think similar a vertex" approach. A dominant computing model inward "think similar a vertex" approach is the Gather-Apply-Scatter (GAS) model. You tin brush upwardly on graph processing engines past times reading my reviews of Pregel together with Facebook graph processing.Modeling ML problems equally bipartite graphs
Many ML problems tin last modeled alongside graphs together with attacked via iterative computation on the graph vertices. The Matrix Factorization (MF) algorithm, used inward recommendation systems, tin last modeled equally a computation on a bipartite user-item graph where each vertex corresponds to a user or an especial together with each border corresponds to a user's rating of an item.A topic-modeling algorithm similar Latent Drichlet Allocation (LDA) tin last modeled equally a document-word graph. If a document contains a word, at that topographic point is an border betwixt them; the information on that border are the topics of the discussion inward the document. Even logistic regression tin last modeled equally a sample-feature graph.
Gaps inward addressing ML via graph processing
1. The graphs that model ML problems oft receive got bi-partite nature together with heterogeneous vertices, alongside distinct roles (e.g., user vertices together with especial vertices). However, the touchstone graph model used inward graph processing frameworks assumes a homogeneous laid of vertices.2. For ML computation, an iteration of a graph computation mightiness involve multiple rounds of propagation betwixt dissimilar types of vertices, rather than a uncomplicated serial of GAS phases. The touchstone GAS model is unable to limited such computation patterns efficiently.
3. Machine learning frameworks receive got been shown to produce goodness from the Stale Synchronous Parallel (SSP) model, a relaxed consistency model alongside bounded staleness to improve parallelism. The graph processing engines usage Bulk Synchronous Parallel (BSP) model past times default.
TUX2 Design
To address the gaps identified above, TUX21. supports heterogeneity inward the information model,
2. advocates a novel graph model, MEGA (Mini-batch, Exchange, GlobalSync, together with Apply), that allows flexible composition of stages, and
3. supports SSP inward execution scheduling.
Next nosotros speak over the basic pattern elements inward TUX2, together with how the inward a higher house three capabilities are built on them.
The vertex-cut approach
TUX2 uses the vertex-cut approach (introduced inward PowerGraph), where the border laid of a high-degree vertex tin last dissever into multiple partitions, each maintaining a replica of the vertex. One of these replicas is designated the master; it maintains the original version of the vertex's data. All the remaining replicas are called mirrors, together with each maintains a local cached copy.Vertex-cut is real useful for implementing the parameter-server model: The original versions of all vertices' information tin last treated equally the distributed global nation stored inward a parameter server. The mirrors are distributed to workers, which likewise has the instant type of vertices together with usage the mirror vertices to iterate on these instant type of vertices.
Wait, the instant type of vertices? Yes, hither nosotros harken dorsum to the bipartite graph model. Recall that nosotros had bipartite graph alongside heterogeneous vertices, alongside around vertices having higher degrees. Those higher flat vertices are original vertices together with held at the server, together with the depression flat vertices are data/training for those original vertices together with they cache the original vertices equally mirror vertices together with prepare on them. And, inward around sense, the partitions of low-order vertex type inward the bipartite graph corresponds to mini-batch.
The newspaper has the next to say on this. In a bipartite graph, TUX2 tin enumerate all edges past times scanning alone vertices of 1 type. The choice of which type to enumerate sometimes has pregnant performance implications. Scanning the vertices alongside mirrors inward a mini-batch tends to Pb to a to a greater extent than efficient synchronization step, because these vertices are placed contiguously inward an array. In contrast, if TUX2 scans vertices without mirrors inward a mini-batch, the mirrors that acquire updated for the other vertex type during the scan volition last scattered together with thus to a greater extent than expensive to locate. TUX2 thence allows users to specify which laid of vertices to enumerate during the computation.
Each sectionalization is managed past times a procedure that logically plays both
- a worker role, to enumerate vertices inward the sectionalization together with propagate vertex information along edges, and
- a server role, to synchronize states betwixt mirror vertices together with their corresponding masters.
Figure three illustrates how TUX2 organizes vertex information for a bipartite graph, using MF on a user-item graph equally an example. Because user vertices receive got much smaller flat inward general, alone especial vertices are dissever past times vertex-cut partitioning. Therefore, a original vertex array inward the server role contains alone especial vertices, together with the worker role alone manages user vertices. This way, at that topographic point are no mirror replicas of user vertices together with no distributed synchronization is needed. In the worker role, the mirrors of especial together with user vertices are stored inward ii assort arrays.
In each partition, TUX2 maintains vertices together with edges inward assort arrays. Edges inward the border array are grouped past times origin vertex. Each vertex has an index giving the offset of its edge-set inward the border array. Each border contains information such equally the id of the sectionalization containing the destination vertex together with the index of that vertex inward the corresponding vertex array. This graph information construction is optimized for traversal together with outperforms vertex indexing using a lookup table. Figure 2 shows how information are partitioned, stored, together with assigned to execution roles inward TUX2.
Scheduling minibatches alongside SSP
TUX2 executes each iteration on a minibatch alongside a specified size. Each worker get-go chooses a laid of vertices or edges equally the electrical flow minibatch to execute on. After the execution on the mini-batch finishes, TUX2 acquires around other laid of vertices or edges for the side past times side minibatch, oft past times continuing to enumerate contiguous segments of vertex or border arrays.TUX2 supports SSP inward the mini-batch granularity. It tracks the progress of each mini-batch iteration to enable computation of clocks. A worker considers clock t completed if the corresponding mini-batch is completed on all workers (including synchronizations betwixt masters together with mirrors) together with if the resulting update has been applied to together with reflected inward the state. A worker tin execute a undertaking at clock t alone if it knows that all clocks upwardly to t−s−1 receive got completed, where s is the allowed slack.
The MEGA model
TUX2 introduces a novel stage-based MEGA model, where each stage is a computation on a laid of vertices together with their edges inward a graph. Each stage has user-defined functions (UDF) to last applied on the vertices or edges accessed during it. MEGA defines 4 types of stage: Mini-batch, Exchange, GlobalSync, together with Apply.MEGA allows users to build an arbitrary sequence of stages. Unlike GAS, which needs to last repeated inward social club (i.e., GAS-GAS-GAS-GAS), inward MEGA you lot tin flexibly mix together with tally (e.g., E-A-E-A-G). For example, inward algorithms such equally MF together with LDA, processing an border involves updating both vertices. This requires ii GAS phases, simply tin last accomplished inward 1 Exchange stage inward META. For LR, the vertex information propagations inward both directions should last followed past times an Apply phase, simply no Scatter phases are necessary; this tin last avoided inward the MEGA model because MEGA allows an arbitrary sequence of stages.
Below are examples of Matrix factorization (MF) together with Latent Dirichlet Allocation (LDA) programmed alongside the META model. (LDA's stage sequence is the same equally MF's.)
Implementation together with Evaluation
TUX2 is implemented inward 12,000 lines of C++ code. TUX2 takes graph information inward a collection of text files equally input. Each procedure picks a assort subset of those files together with performs bipartite-graph-aware algorithms to sectionalization the graph inward a distributed way. Each sectionalization is assigned to, together with stored locally with, a process. Unfortunately the evaluations alongside TUX2 produce non accept into concern human relationship graph partitioning time, which tin last real high.The evaluations exhibit that information layout matters greatly inward the performance of ML algorithms. Figure 8 compares the performance of BlockPG, MF, together with LDA alongside ii dissimilar layouts: 1 an array-based graph information layout inward TUX2 together with the other a hash-table-based lay-out oft used inward parameter-server-based systems (but implemented inward TUX2 for comparison). The y-axis is the average running fourth dimension of 1 iteration for BlockPG, together with of 10 iterations for MF together with LDA to exhibit the numbers on a similar scale. These results exhibit that the graph layout improves performance past times upwardly to 2.4× over the hash-table-based layout.
The newspaper likewise includes a comparing alongside Petuum, simply the evaluations receive got several caveats. The evaluations produce non include comparing of convergence/execution time; execution fourth dimension per iteration does non ever create upwardly one's heed the convergence time. The evaluations produce non accept into concern human relationship the partitioning fourth dimension of the graph for TUX2. And finally, around comparisons used early on unpatched version of Petuum MF algorithm whose information placement issues are resolved later.
MAD questions
1. What is the internet gain here?I similar this paper; it made me inquire together with ponder on many questions, which is good.
I don't shout upwardly TUX2 pushes the nation of the fine art inward ML. ML processing frameworks are already real efficient together with full general alongside the iterative parameter-server computing model, together with they are getting ameliorate together with to a greater extent than fine grained.
On the other hand, I shout upwardly TUX2 is valuable because it showed how the high-level graph computing frameworks tin last adapted to implement the low-level parameter-server approach together with address ML preparation problems to a greater extent than efficiently. This may supply around advantages for problems that are/need to last represented equally graphs, such equally for performing ML preparation on Sparql information stores.
Moreover past times using higher-level primitives, TUX2 provides around rest of programmability. I gauge this may last leveraged farther to accomplish around plug together with play programming of ML for sure shape of programs.
So I respect this to last a conceptually real satisfying newspaper equally it bridges the graph processing model to parameter-server model. I am less sure nearly the practicality part.
2. How does graph processing frameworks compare alongside dataflow frameworks?
There are large differences betwixt dataflow frameworks together with graph processing frameworks. In the dataflow model, there is a symbolic computation graph, the graph nodes correspond mathematical operations, spell the graph edges correspond the information that menstruum betwixt the operations. That is a real dissimilar model than the graph processing model here.
In MEGA, at that topographic point are alone 4 stages, where the Apply stage tin accept inward user defined functions. This is higher-level (and arguably to a greater extent than programming friendly) than a dataflow framework such equally TensorFlow which has many hundreds of predefined operators equally vertices.
3. How does TUX2 apply to Deep Learning (DL)?
The newspaper does non speak nearly whether TUX2 tin apply to DL together with how it tin apply.
It may last possible to brand DL fit the TUX2 model alongside around stretching. Deep neural network (DNN) layers (horizontally or vertically partitioned) could last the high-rank vertices concur inward the servers. And the images are low-ranked vertices concur inward partitions.
But this volition require treating the DNN partions equally a meta-vertex together with schedule executions for each sub-vertes inward the meta-vertex inward 1 cycle. I receive got no clue nearly how to brand backpropagation piece of work hither though.
Moreover, for each image, the ikon may demand to link to entire NN, so the bipartite graph may collapse into a piddling 1 together with piddling data-parallelism. It may last possible to brand the convolutional layers tin last distributed. It may fifty-fifty last possible to insert early on exits together with prepare that way.
So, it may last possible simply it is surely non straightforward. I am non fifty-fifty touching the bailiwick of the performance of such a system.
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