From dc6b3b5a687b9c8d3c789038cb64e8cf5ad6fc7c Mon Sep 17 00:00:00 2001 From: wang Date: Mon, 27 Feb 2023 17:29:54 +0800 Subject: [PATCH 1/8] add pregel doc --- .../vertex_centric_models.md | 55 +++++++++++++++--- docs/images/pregel.png | Bin 0 -> 16089 bytes 2 files changed, 46 insertions(+), 9 deletions(-) create mode 100644 docs/images/pregel.png diff --git a/docs/analytical_engine/vertex_centric_models.md b/docs/analytical_engine/vertex_centric_models.md index f371c41f4594..600cccc3401e 100644 --- a/docs/analytical_engine/vertex_centric_models.md +++ b/docs/analytical_engine/vertex_centric_models.md @@ -1,20 +1,57 @@ -# Vertex Centric Models +# Vertex-Centric Model -biref intro to vertex centric models, history; +In a single machine environment, developers can easily implement graph analytics algorithms as they have a global view of the graph and can freely iterate through all vertices and edges. When the size of graph data grows beyond the memory capacity of a single machine, graph data must be partitioned to distributed memory, leading to the indivisibility of the whole graph structure. -## GAS Model +To allow developers to succinctly express graph analytics algorithms under such environment, the *vertex-centric programming model* have been developed. Specifically, a graph analytics algorithm iteratively executes a user-defined program over vertices of a graph. The user-defined vertex program +typically takes data from other vertices as input, and the +resultant output of a vertex is sent to other vertices. Vertex programs are +executed iteratively for a certain number of rounds, or until a convergence condition is +satiisfied. As opposed to *global* perspective of the graph, vertex-centric models +employ a local, vertex-oriented perspective. -intro and figure +The philosophy of vertex-centric model encourages many programming models, including the [Pregel model](https://research.google/pubs/pub37252/) proposed by Google and the [GAS model](https://www.usenix.org/conference/osdi12/technical-sessions/presentation/gonzalez). These programming models +have widely applied in various graph processing systems, such as Giraph, GraphX and PowerGraph. ## Pregel Model -intro and figure +Pregel was first introduced in a [SIGMOD paper](https://dl.acm.org/doi/10.1145/1807167.1807184) published by Google in 2010. A graph analytics algorithm with the Pregel model consists of a sequence of iterations( called *supersteps*). + +> “During a superstep the framework invokes a user-defined function for each vertex, conceptually in parallel. The function specifies behavior at a single vertex *V* and a single superstep *S*. It can read messages sent to *V* in superstep *S − 1*, send messages to other vertices that will be received at superstep *S + 1*, and modify the state of *V* and its outgoing edges. Messages are typically sent along outgoing edges, but a message may be sent to any vertex whose identifier is known.” + +:::{figure-md} + +The Pregel model + +The Pregel model. +::: + +The iterations terminate until no messages are sent from any vertex, indicating a halt. + +With the Pregel model, the vertex program of single source shortest paths (SSSP) is expressed as follows. + +```python +def VertexProgramForSSSP(): + # receive and merge incoming messages + incoming_msgs = ReceiveMessages() + merged_msg = Reduce(incoming_msgs, MIN) + + # update vertex property + if dist > merged_msg: + dist = merged_msg + + # send messages to neighbors + for neighbor in neighbors: + if dist + edge_weight < neighbor_dist: + SendMessage(neighbor, dist + edge_weight) +``` ## Simulation of Pregel Model in Analytical Engine -As we proved in the paper, the analytical engine is able to simulate the vertex centric models. We have implemented the support for Pregel model in the analytical engine, and you can use the Pregel API to write your own algorithms. In addition, if you already have your graph applications implemented in Giraph or GraphX, you can run them on GraphScope directly. Better still, the analytical engine can achieve better performance than Giraph and GraphX. +As we proved in this [paper](https://dl.acm.org/doi/pdf/10.1145/3035918.3035942), the analytical engine of GraphScope is able to simulate the vertex centric models. We have implemented the support for Pregel model in the analytical engine, and you can use the Pregel APIs to write your own algorithms. In addition, if you already have your graph applications implemented in Giraph or GraphX, you can run them on GraphScope directly. Better still, the analytical engine can achieve better performance than Giraph and GraphX. Please refer to related tutorials. -- t1 -- t2 -- t3 \ No newline at end of file + +- [Tutorial: Run Giraph Applications on GraphScope](https://graphscope.io/docs/latest/analytical_engine/tutorial_run_giraph_apps.html) +- [Tutorial: Run GraphX Applications on GraphScope](https://graphscope.io/docs/latest/analytical_engine/tutorial_run_graphx_apps.html) \ No newline at end of file diff --git a/docs/images/pregel.png b/docs/images/pregel.png new file mode 100644 index 0000000000000000000000000000000000000000..d8d2eb3ab5779c87e3e84c15d87b314b08f7951d GIT binary patch literal 16089 zcmeIZXEdDQ_cqFCqs9=?8J(yZ(MyCu^b*l~PjsRNG3w~OOo%QdqW50I=q0-7L=B<_ zA?Hbc@BhEfS?_z+I%~b>jO0~FrXMp@TWRHm_JPLGwy$xztj9VOQou+vtGS8eW0^J;eqM}_xdpB zKMMC@TUPkPn$crMZpq9U#ja40t3|Hq<^5?Is+?c43W-N5l4<<}GXydFK#RZeR8IYA z6`Hm>5)d4g`YLE9HRgEvdiLkEQQf)v^yRc3oQ?`pAqZlQ{r}saTy;`cBxhP4Ut1)a zCI3Crz9MDJ4pyji`Rl9j50aOrf!*yrb-J=u=VhsWm2@!}ES+E-A_Ohco&^#htQM(3 zKA{4CoPvBki1(6#IiF>+P$l3@E%PF4iW2_1J)ptvo%cM0lLp)>(tgD;k-%TiF@TUK 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ze;!y3d6>o5*#DUen31sZqY1@`YOwU-U6ZQ#zL_I85Vqdsr|*s_+_0$K)Q0OfGuhwo zn=k@bgQBD7nARS9dYO3#)Q&Dhl=gsGAwk_=^?fbw;R)mDS`|wU!soph0Z9*xE%k@e zOOM9L%iU9hmDGEvEi1=Ia ziGYerk3dX-k}AA&)kpzK0AAHM&f Date: Mon, 27 Feb 2023 17:38:06 +0800 Subject: [PATCH 2/8] fix figs --- docs/analytical_engine/vertex_centric_models.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/analytical_engine/vertex_centric_models.md b/docs/analytical_engine/vertex_centric_models.md index 600cccc3401e..3afff091bedb 100644 --- a/docs/analytical_engine/vertex_centric_models.md +++ b/docs/analytical_engine/vertex_centric_models.md @@ -20,9 +20,9 @@ Pregel was first introduced in a [SIGMOD paper](https://dl.acm.org/doi/10.1145/1 :::{figure-md} -The Pregel model + width="100%"> The Pregel model. ::: From 81c9bfde092e7fd9091d71d0bcdbd4fcda467937 Mon Sep 17 00:00:00 2001 From: wang Date: Mon, 27 Feb 2023 18:01:25 +0800 Subject: [PATCH 3/8] minor --- docs/analytical_engine/vertex_centric_models.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/analytical_engine/vertex_centric_models.md b/docs/analytical_engine/vertex_centric_models.md index 3afff091bedb..3ec4c0e729bd 100644 --- a/docs/analytical_engine/vertex_centric_models.md +++ b/docs/analytical_engine/vertex_centric_models.md @@ -29,6 +29,8 @@ The Pregel model. The iterations terminate until no messages are sent from any vertex, indicating a halt. +The vertex function can be invoked at each vertex in parallel, since individual vertices communicate via message-passing. + With the Pregel model, the vertex program of single source shortest paths (SSSP) is expressed as follows. ```python From bbd08cd48a3cffb680b5ac47a446037b5921a349 Mon Sep 17 00:00:00 2001 From: wang Date: Tue, 28 Feb 2023 10:16:25 +0800 Subject: [PATCH 4/8] add PIE doc --- .../programming_model_pie.md | 72 ++++++++++++++++++- 1 file changed, 70 insertions(+), 2 deletions(-) diff --git a/docs/analytical_engine/programming_model_pie.md b/docs/analytical_engine/programming_model_pie.md index aab140a24001..769b836b83f9 100644 --- a/docs/analytical_engine/programming_model_pie.md +++ b/docs/analytical_engine/programming_model_pie.md @@ -1,6 +1,74 @@ # Programming Model: PIE -- Workflow, PEval and IncEval: intro -- Under the hook +Although the [vertex-centric programming model](https://graphscope.io/docs/latest/analytical_engine/vertex_centric_models.html) can express various graph analytics algorithms, existing sequential (single-machine) graph algorithms have to be modified to comply with the “think like a vertex” principle, making parallel graph computation a privilege for experienced users only. In addition, the performance of graph algorithms with vertex-centric model is sub-optimal in many cases: each vertex only has information about its 1-hop neighbors, and thus information is propagated through the graph slowly, one hop at a time. As a result, it may take many computation iterations to propagate a piece of information from a source to a destination. + +## What is the PIE Model? + +To address the abovementioned problems, we proposed a new programming model PIE (PEval-IncEval-Assemble) in a [SIGMOD paper](https://dl.acm.org/doi/10.1145/3035918.3035942) published in 2017. Different from the vertex-centric, the PIE model can automatically parallelize existing sequential graph algorithms, with only some small changes. This makes parallel graph computations accessible to users who know conventional graph algorithms covered in college textbooks, and there is no need to recast existing graph algorithms into a new model. + +Specifically, in the PIE model, users only need to provide three functions, + +- (1) *PEval*, a sequential (single-machine) function for given a query, computes the answer on a local partition; +- (2) *IncEval*, a sequential incremental function, computes changes to the old output by treating incoming messages as updates; and +- (3) *Assemble*, which collects partial answers, and combines them into a complete answer. + +:::{figure-md} + +The PIE model + +The PIE model. +::: + +## Workflow of PIE + +The PIE model works on a graph *G* and each worker maintains a partition of *G*. Given a query, each worker first executes *PEval* against its local partition, to compute partial answers in parallel. Then each worker may exchange partial results with other workers via synchronous message passing. Upon receiving messages, each worker incrementally computes *IncEval*. The incremental step iterates until no further messages can be generated. At this point, *Assemble* pulls partial answers and assembles the final result. In this way, the PIE model parallelizes existing sequential graph algorithms, without revising their logic and workflow. + +In this model, users do not need to know the details of the distributed setting while processing big graphs in a cluster, and the PIE model auto-parallelizes the graph analytics tasks across +a cluster of workers, based on a fixpoint computation. Under a monotonic condition, it guarantees to +converge with correct answers as long as the three sequential algorithms provided are correct. + +The following pseudo-code shows how SSSP is expressed in the PIE model, where the Dijkstra’s algorithm is directly used for the computation of parallel SSSP. + +```python +def dijkstra(g, vals, updates): + heap = VertexHeap() + for i in updates: + vals[i] = updates[i] + heap.push(i, vals[i]) + + updates.clear() + + while not heap.empty(): + u = heap.top().vid + distu = heap.top().val + heap.pop() + for e in g.get_outgoing_edges(u): + v = e.get_neighbor() + distv = distu + e.data() + if vals[v] > distv: + vals[v] = distv + if g.is_inner_vertex(v): + heap.push(v, distv) + updates[v] = distv + return updates + +def PEval(source, g, vals, updates): + for v in vertices: + updates[v] = MAX_INT + updates[source] = 0 + dijkstra(g, vals, updates) + +def IncEval(source, g, vals, updates): + dijkstra(g, vals, updates) +``` + + + + + + +TODO(wanglei): Under the hook - MessageBuffer, - ParallelExecutor, \ No newline at end of file From 048fd6dfcd87cb3e899604fd050f2adab9674fa7 Mon Sep 17 00:00:00 2001 From: wang Date: Tue, 28 Feb 2023 10:21:23 +0800 Subject: [PATCH 5/8] minor --- docs/analytical_engine/programming_model_pie.md | 2 +- docs/analytical_engine/vertex_centric_models.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/analytical_engine/programming_model_pie.md b/docs/analytical_engine/programming_model_pie.md index 769b836b83f9..b6714f870398 100644 --- a/docs/analytical_engine/programming_model_pie.md +++ b/docs/analytical_engine/programming_model_pie.md @@ -16,7 +16,7 @@ Specifically, in the PIE model, users only need to provide three functions, The PIE model + width="80%"> The PIE model. ::: diff --git a/docs/analytical_engine/vertex_centric_models.md b/docs/analytical_engine/vertex_centric_models.md index 3ec4c0e729bd..a68fdd21deaa 100644 --- a/docs/analytical_engine/vertex_centric_models.md +++ b/docs/analytical_engine/vertex_centric_models.md @@ -14,7 +14,7 @@ have widely applied in various graph processing systems, such as Giraph, GraphX ## Pregel Model -Pregel was first introduced in a [SIGMOD paper](https://dl.acm.org/doi/10.1145/1807167.1807184) published by Google in 2010. A graph analytics algorithm with the Pregel model consists of a sequence of iterations( called *supersteps*). +Pregel was first introduced in a [SIGMOD paper](https://dl.acm.org/doi/10.1145/1807167.1807184) published by Google in 2010. A graph analytics algorithm with the Pregel model consists of a sequence of iterations(called *supersteps*). > “During a superstep the framework invokes a user-defined function for each vertex, conceptually in parallel. The function specifies behavior at a single vertex *V* and a single superstep *S*. It can read messages sent to *V* in superstep *S − 1*, send messages to other vertices that will be received at superstep *S + 1*, and modify the state of *V* and its outgoing edges. Messages are typically sent along outgoing edges, but a message may be sent to any vertex whose identifier is known.” From 62b9671cc1352aee7ec0cef82b1c6bff66943a84 Mon Sep 17 00:00:00 2001 From: wang Date: Tue, 28 Feb 2023 10:24:20 +0800 Subject: [PATCH 6/8] fix typos --- docs/analytical_engine/vertex_centric_models.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/analytical_engine/vertex_centric_models.md b/docs/analytical_engine/vertex_centric_models.md index a68fdd21deaa..91357340c165 100644 --- a/docs/analytical_engine/vertex_centric_models.md +++ b/docs/analytical_engine/vertex_centric_models.md @@ -6,7 +6,7 @@ To allow developers to succinctly express graph analytics algorithms under such typically takes data from other vertices as input, and the resultant output of a vertex is sent to other vertices. Vertex programs are executed iteratively for a certain number of rounds, or until a convergence condition is -satiisfied. As opposed to *global* perspective of the graph, vertex-centric models +satisfied. As opposed to *global* perspective of the graph, vertex-centric models employ a local, vertex-oriented perspective. The philosophy of vertex-centric model encourages many programming models, including the [Pregel model](https://research.google/pubs/pub37252/) proposed by Google and the [GAS model](https://www.usenix.org/conference/osdi12/technical-sessions/presentation/gonzalez). These programming models From 65562cda572cbb0f0e603046bb5c32c9bfe76fc3 Mon Sep 17 00:00:00 2001 From: wang Date: Tue, 28 Feb 2023 14:36:09 +0800 Subject: [PATCH 7/8] add gas --- .../vertex_centric_models.md | 42 +++++++++++------- docs/images/gas.png | Bin 0 -> 153103 bytes 2 files changed, 27 insertions(+), 15 deletions(-) create mode 100644 docs/images/gas.png diff --git a/docs/analytical_engine/vertex_centric_models.md b/docs/analytical_engine/vertex_centric_models.md index 91357340c165..9374e6ef066a 100644 --- a/docs/analytical_engine/vertex_centric_models.md +++ b/docs/analytical_engine/vertex_centric_models.md @@ -2,7 +2,7 @@ In a single machine environment, developers can easily implement graph analytics algorithms as they have a global view of the graph and can freely iterate through all vertices and edges. When the size of graph data grows beyond the memory capacity of a single machine, graph data must be partitioned to distributed memory, leading to the indivisibility of the whole graph structure. -To allow developers to succinctly express graph analytics algorithms under such environment, the *vertex-centric programming model* have been developed. Specifically, a graph analytics algorithm iteratively executes a user-defined program over vertices of a graph. The user-defined vertex program +To allow developers to succinctly express graph analytics algorithms under such environment, the *vertex-centric programming model* have been developed, with the philosophy of "think like a vertex". Specifically, a graph analytics algorithm iteratively executes a user-defined program over vertices of a graph. The user-defined vertex program typically takes data from other vertices as input, and the resultant output of a vertex is sent to other vertices. Vertex programs are executed iteratively for a certain number of rounds, or until a convergence condition is @@ -33,22 +33,34 @@ The vertex function can be invoked at each vertex in parallel, since individual With the Pregel model, the vertex program of single source shortest paths (SSSP) is expressed as follows. -```python -def VertexProgramForSSSP(): - # receive and merge incoming messages - incoming_msgs = ReceiveMessages() - merged_msg = Reduce(incoming_msgs, MIN) - - # update vertex property - if dist > merged_msg: - dist = merged_msg - - # send messages to neighbors - for neighbor in neighbors: - if dist + edge_weight < neighbor_dist: - SendMessage(neighbor, dist + edge_weight) +```c++ +void Compute(MessageIterator* msgs) { + int mindist = IsSource(vertex_id()) ? 0 : INF; + for (; !msgs->Done(); msgs->Next()) + mindist = min(mindist, msgs->Value()); + if (mindist < GetValue()) { + *MutableValue() = mindist; + OutEdgeIterator iter = GetOutEdgeIterator(); + for (; !iter.Done(); iter.Next()) + SendMessageTo(iter.Target(), mindist + iter.GetValue()); +} + VoteToHalt(); +} ``` +## GAS Model + +However, the performance of Pregel drops dramatically when facing natural graphs which follow a power-law distribution. To solve this problem, [PowerGraph](https://www.usenix.org/conference/osdi12/technical-sessions/presentation/gonzalez) proposed the GAS (Gather-Apply-Scatter) programming model for the vertex-cut graph partitioning strategy. The *Gather* function runs locally on each partition and then one accumulator is sent from each mirror to the master. The master runs the *Apply* function and then sends the updated vertex data to all mirrors. Finally, the *Scatter* phase is run in parallel on mirrors to update the data on adjacent edges. + +:::{figure-md} + +The GAS model + +The GAS model. +::: + ## Simulation of Pregel Model in Analytical Engine As we proved in this [paper](https://dl.acm.org/doi/pdf/10.1145/3035918.3035942), the analytical engine of GraphScope is able to simulate the vertex centric models. We have implemented the support for Pregel model in the analytical engine, and you can use the Pregel APIs to write your own algorithms. In addition, if you already have your graph applications implemented in Giraph or GraphX, you can run them on GraphScope directly. 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zEgg`)TgF}A=bWGOn}Q}-Bze0<^yi@WYEp;&?c}@daGooXE*a#phDm4hi)4GrGE%re{?ml4>rf*1k=_R8| z(nQ>*AsYty(iU9i$Fs%KR+hJRn=|8Hs+5S+@}&=qA*5?O@sz12!m-9nV#JVPvbIh6 z(p7Aiu4utvpycCuz>bQAnBe#0tImi)=q?Zvysu;E8r<&QV^jm=CG1 z?ADc*7jI|}+H?^p0!5$*6oDd81bz(ySJ+7ZYq02n6oDd81d2crC;~;`W(bsY-3;0` zTm*_h5hwyhpa>L!g+NKy!lJPvPy~uV5hwyhpa|Ryfs(G9LEDClKoKYcMW6^2fg-RF Z_ycV<_M#CH1#|!a002ovPDHLkV1h)Mtbzal literal 0 HcmV?d00001 From 31cbc3785c33eb6dacd2a729faad325ea0742a47 Mon Sep 17 00:00:00 2001 From: wang Date: Tue, 28 Feb 2023 14:39:15 +0800 Subject: [PATCH 8/8] fig size --- docs/analytical_engine/vertex_centric_models.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/analytical_engine/vertex_centric_models.md b/docs/analytical_engine/vertex_centric_models.md index 9374e6ef066a..0f8435150323 100644 --- a/docs/analytical_engine/vertex_centric_models.md +++ b/docs/analytical_engine/vertex_centric_models.md @@ -22,7 +22,7 @@ Pregel was first introduced in a [SIGMOD paper](https://dl.acm.org/doi/10.1145/1 The Pregel model + width="50%"> The Pregel model. ::: @@ -56,7 +56,7 @@ However, the performance of Pregel drops dramatically when facing natural graphs The GAS model + width="50%"> The GAS model. :::