Making Time-stepped Applications Tick in the Cloud Cornell University *University of Copenhagen (DIKU)
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Making Time-stepped Applications Tick in the Cloud Tao Zou, Guozhang Wang, Marcos Vaz Salles*, David Bindel, Alan Demers, Johannes Gehrke, Walker White Cornell University *University of Copenhagen (DIKU) 1 Time-Stepped Applications • Executed with parallelism organized into logical ticks. • Implemented using Bulk Synchronous Parallel (BSP) Model Processors Tick Tickπ + π π P1 P2 P3 …… Pn−1 Pn …… Local Computation Communication Global Barrier 2 Running Example: Fish Simulation Tick π +π • Behavioral Simulation – Traffic simulation – Simulation of groups of animals 3 Other Time-Stepped Applications • Iterative Graph Processing • Matrix Computation 4 Why Run Scientific Applications in the Cloud? • Elasticity • Cost Saving • Instant Availability Avoid jobs queuing for days 5 What Does Cloud Infrastructure Imply ο Unstable network latencies Local Cluster VS Large Small Instance EC2 Cluster Instance • Virtualization • Lack of network performance isolation 6 Time-Stepped Applications under Latency Jitter • Sensitive to latencies Processors P1 P2 P3 …… Pn−1 Pn …… • Remove unnecessary barriers – Jitter still propagates Local Computation Communication Barrier Synchronization 7 Problem • Time-stepped applications • Unstable latencies • Solution space – Improve the networking infrastructure • Recent proposals only tackle bandwidth problems – Make applications more resistant to unstable latencies 8 Talk Outline • • • • Motivation Our Approach Experimental Results Conclusions 9 Why not Ad-Hoc Optimizations? • Disadvantages – No Generality Goal: Applicable to all time-stepped applications – No Ease of Programming Goal: Transparent optimization and communication – Error-Prone Goal: Correctness guarantee • Programming Model + Jitter-tolerant Runtime 10 Talk Outline • Motivation • Our Approach – Programming Model – Jitter-tolerant Runtime • Experimental Results • Conclusions 11 Data Dependencies: What to Communicate • Read Dependency – Example: How far can a fish see? • Write Dependency – Example: How far can a fish move? Write Read • Key: Modeling Dependencies 12 Programming Model Modeling State • Motivated by thinking of the applications as distributed database system • Application state: Set of tuples – Fish ο tuple – Fish school ο application state • Selection over state: Query – 2D range query over fish school 13 Programming Model Modeling Data Parallelism • Partition Function: PART(π) → Q1 , Q2 , … , Qn Q1 Q 2 Q 3 Q4 Q5 Q6 Q7 Q8 Q9 14 Programming Model Modeling Computation • Parallel Computation: STEP ToCompute Qi , Context Q1 Q 2 Q 3 Q4 ππ Q6 Q7 Q8 Q9 • Context: How large? 15 Programming Model Modeling Dependencies: R • Read Dependency: R(Q) R(Q) Q Q – Contains all necessary tuples in context to compute Q STEP Q, R(Q) 16 Programming Model Modeling Dependencies: IR • STEP QQ ?St , RR(Q) Q S t • Inverse Read Dependency: IR(Q) Q IR(Q) – Contains all tuples that can be computed with Q as context STEP IR(Q), Q – IR ≈ R−1 17 Programming Model Modeling Dependencies: W • Write Dependency: W(Q) W(Q) Q Q – Contains all tuples generated by computing Q 18 Programming Model Modeling Dependencies: IW • Inverse Write Dependency: IW(Q) Q IW(Q) – Contains all tuples in the next tick after computing Q – IW ≈ W −1 19 Programming Model: All together • • • • PART – data parallelism STEP – computation R, IR – read dependencies W, IW– write dependencies • Remarks: Q R(Q) IR(Q) PageRank – Users inherently think in terms of dependencies – Not limited to spatial properties 20 Talk Outline • Motivation • Our Approach – Programming Model – Jitter-tolerant Runtime • Experimental Results • Conclusions 21 Jitter-tolerant Runtime • Input: Functions defined in programming model • Output: Parallel computation results • Requirement: Efficiency and Correctness 22 Runtime Dependency Scheduling Tick π Compute Q Q Q IW(Q) IR β IW Q IR(Q) 2 IRIR β βIW IW Q Q Send out updates Wait for messages All message received Tick π + π Compute IR βIR IW QQ Compute ? Compute Q − IR β IW Q Send out updates …… Tick π + π Compute IR β IW 2 Q No. Incoming message may contain updates…… to Q. IW(Q) is not influenced by the Intuition: schedule computation formessages future ticks when delayed 23 Runtime Computational Replication Tick π Q W β R(Q) Compute Q Send out updates Wait for messages Q Compute W β R(Q) − Q Tick π + π Compute Q …… • Intuition: enlarge region to compute contents of delayed messages. • W β R Q , (W β R)2 Q , … , (W β R)π Q 24 Our Approach: Summary • Programming model captures – Application state – Computation logic – Data dependencies • Jitter-tolerant runtime – Dependency scheduling – Computational replication 25 Talk Outline • Motivation • Our Approach – Programming Model – Jitter-tolerant Runtime • Experimental Results • Conclusions 26 Experimental Setup • A prototype framework – Jitter-tolerant runtime • MPI for communication – Three different applications • A ο¬sh school behavioral simulation • A linear solver using the Jacobi method • A message-passing algorithm computes PageRank • Hardware Setup – Up to 100 EC2 large instances (m1.large) • 2.26GHz Xeon cores with 6MB cache • 7.5GB main memory 27 Methodology • Observation: Temporal variation in network performance • Solution – Execute all settings in rounds of fixed order – At least 20 consecutive executions of these rounds 28 Effect of Optimization: Fish Sim • Baseline: Local Synchronization; Sch: Dependency Scheduling; Rep: Computational Replication 29 Effect of Optimization: Jacobi • Baseline: Local Synchronization; Sch: Dependency Scheduling; Rep: Computational Replication 30 Scalability: Fish Simulation • Baseline: Local Synchronization; Sch: Dependency Scheduling; Rep: Computational Replication 31 Conclusions Questions? Thank you! • Latency jitter is a key characteristic of today’s cloud environments. • Programming model + jitter-tolerant runtime – Good performance under latency jitter – Ease of programming – Correctness • We have released our framework as a public Amazon AMI: http://www.cs.cornell.edu/bigreddata/games/. • Our framework will be used this fall in CS 5220 (Applications of Parallel Computers) at Cornell. 32
