Graph Processing & Bulk Synchronous Parallel Model
Graph Processing & Bulk Synchronous Parallel Model
Graph Processing & Bulk Synchronous Parallel Model
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Pregel <br />
• Input: A directed graph G. <br />
Each vertex is associated with an id and a value. <br />
Edges may also contain values. <br />
• Edges are not a first class ciFzen – they have no associated <br />
computaFon <br />
– VerFces can modify its state/edge state/edge set <br />
• ComputaFon finishes when <br />
ciently<br />
all verFces enter the inacFve state <br />
erant platform with an API that is su<br />
xpress arbitrary graph algorithms. This paper<br />
e resulting system, called Pregel 1 , and reports<br />
ce with it.<br />
evel organization of Pregel programs is inspired<br />
<strong>Bulk</strong> <strong>Synchronous</strong> <strong>Parallel</strong> model [45]. Pregel<br />
s consist of a sequence of iterations, called suring<br />
a superstep the framework invokes a usertion<br />
for each Lecture vertex, 14 : conceptually 590.02 Spring in 13 parallel. <br />
specifies behavior at a single vertex V and a<br />
Vote to halt<br />
Active<br />
Inactive<br />
Message received<br />
Figure 1: Vertex State Machine<br />
12
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