Periodic I/O Scheduling for Supercomputers
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1 Periodic I/O Scheduling for Supercomputers Guillaume Aupy 1, Ana Gainaru 2, Valentin Le Fèvre 3 1 Inria & U. of Bordeaux 2 Vanderbilt University 3 ENS Lyon & Inria PMBS Workshop, November 217 Slides available at
2 IO congestion in HPC systems Some numbers for motivation: Computational power keeps increasing (Intrepid:.56 PFlop/s, Mira: 1 PFlop/s, Aurora: 45 PFlop/s (?)). IO Bandwidth increases at slowlier rate (Intrepid: 88 GB/s, Mira: 24 GB/s, Aurora: 1 TB/s (?)). 1
3 IO congestion in HPC systems Some numbers for motivation: Computational power keeps increasing (Intrepid:.56 PFlop/s, Mira: 1 PFlop/s, Aurora: 45 PFlop/s (?)). IO Bandwidth increases at slowlier rate (Intrepid: 88 GB/s, Mira: 24 GB/s, Aurora: 1 TB/s (?)). In other terms: Intrepid can process 16 GB for every PFlop Mira can process 24 GB for every PFlop Aurora will (?) process 2.2 GB for every PFlop Congestion is coming. 1
4 Burst buffers: the solution? Simplistically: If IO bandwidth available: use it Else, fill the burst buffers When IO bandwidth is available: empty the burst-buffers. If the Burst Buffers are big enough it should work 2
5 Burst buffers: the solution? Simplistically: If IO bandwidth available: use it Else, fill the burst buffers When IO bandwidth is available: empty the burst-buffers. If the Burst Buffers are big enough it should work right? 2
6 Burst buffers: the solution? Simplistically: If IO bandwidth available: use it Else, fill the burst buffers When IO bandwidth is available: empty the burst-buffers. If the Burst Buffers are big enough it should work right? Average I/O occupation: sum for all applications of the volume of I/O transfered, divided by the time they execute, normalized by the peak I/O bandwidth. 2
7 Burst buffers: the solution? Simplistically: If IO bandwidth available: use it Else, fill the burst buffers When IO bandwidth is available: empty the burst-buffers. If the Burst Buffers are big enough it should work right? Average I/O occupation: sum for all applications of the volume of I/O transfered, divided by the time they execute, normalized by the peak I/O bandwidth. Given a set of data-intensive applications running conjointly: on Intrepid have a max average I/O occupation of 25% 2
8 Burst buffers: the solution? Simplistically: If IO bandwidth available: use it Else, fill the burst buffers When IO bandwidth is available: empty the burst-buffers. If the Burst Buffers are big enough it should work right? Average I/O occupation: sum for all applications of the volume of I/O transfered, divided by the time they execute, normalized by the peak I/O bandwidth. Given a set of data-intensive applications running conjointly: on Intrepid have a max average I/O occupation of 25% on Mira have an average I/O occupation of 12 to 3%! 2
9 Previously in IO cong. Online scheduling (Gainaru et al. Ipdps 15): When an application is ready to do I/O, it sends a message to an I/O scheduler; Based on the other applications running and a priority function, the I/O scheduler will give a GO or NOGO to the application. If the application receives a NOGO, it pauses until a GO instruction. Else, it performs I/O. 3
10 Previously in IO cong. App (3) App (2) App (1) bw B Time 3
11 Previously in IO cong. App (3) App (2) App (1) w (3) w (2) w (1) bw B Time 3
12 Previously in IO cong. App (3) App (2) App (1) w (3) w (2) w (1) bw B Time 3
13 Previously in IO cong. App (3) App (2) App (1) w (3) w (2) w (1) bw B Time 3
14 Previously in IO cong. App (3) App (2) App (1) w (3) w (2) w (1) bw B Time 3
15 Previously in IO cong. App (3) App (2) App (1) w (3) w (2) w (1) bw B Time 3
16 Previously in IO cong. App (3) App (2) App (1) bw B w (3) w (2) w (1) w (1) Time 3
17 Previously in IO cong. App (3) App (2) App (1) bw B w (3) w (2) w (1) w (1) Time 3
18 Previously in IO cong. App (3) App (2) App (1) bw B w (3) w (2) w (1) w (1) w (3) Time 3
19 Previously in IO cong. App (3) App (2) App (1) bw B w (3) w (2) w (1) w (1) w (3) w (2) Time 3
20 Previously in IO cong. App (3) App (2) App (1) bw B w (3) w (2) w (1) w (1) w (3) w (3) w (2) w (2) w (1) Time Approx 1% improvement in application performance with 5% gain in system performance on Intrepid. 3
21 This work Assumption: Applications follow I/O patterns 1 that we can obtain (based on historical data for intance). We use this information to compute an I/O time schedule; Each application then knows its GO/NOGO information and uses it to perform I/O. Spoiler: it works very well (at least it seems) 1 periodic pattern, to be defined 4
22 I/O characterization of HPC applis Hu et al Periodicity: computation and I/O phases (write operations such as checkpoints). 2. Synchronization: parallel identical jobs lead to synchronized I/O operations. 3. Repeatability: jobs run several times with different inputs. 4. Burstiness: short burst of write operations. Idea: use the periodic behavior to compute periodic schedules. 5
23 Platform model N unit-speed processors, equipped with an I/O card of bandwidth b Centralized I/O system with total bandwidth B b=.1gb/s/node =B Model instantiation for the Intrepid platform. 6
24 Application Model K periodic applications already scheduled in the system: App (k) (β (k), w (k), vol (k) io ). β (k) is the number of processors onto which App (k) is assigned w (k) is the computation time of a period vol (k) io is the volume of I/O to transfor after the w (k) units of time time (k) io = vol (k) io min(β (k) b, B) App (3) w (3) w (3) w (3) App (2) w (2) w (2) w (2) App (1) w (1) w (1) w (1) Bandwidth B Time 7
25 Objectives If App (k) runs during a total time T k and performs n (k) instances, we define: ρ (k) = w (k) w (k) + time (k) io, ρ (k) = n(k) w (k) T k 8
26 Objectives If App (k) runs during a total time T k and performs n (k) instances, we define: ρ (k) = w (k) w (k) + time (k) io, ρ (k) = n(k) w (k) T k SysEfficiency maximize peak performance (average number of Flops): maximize 1 N K k=1 β(k) ρ (k). 8
27 Objectives If App (k) runs during a total time T k and performs n (k) instances, we define: ρ (k) = w (k) w (k) + time (k) io, ρ (k) = n(k) w (k) T k SysEfficiency maximize peak performance (average number of Flops): maximize 1 N K k=1 β(k) ρ (k). Dilation minimize largest slowdown (fairness between users): minimize max k=1..k ρ (k) ρ (k). 8
28 High-level constraints Applications are already scheduled on the machines: not (yet) our job to do it; 9
29 High-level constraints Applications are already scheduled on the machines: not (yet) our job to do it; We want the schedule information distributed over the applis: the goal is not to add a new congestion point; 9
30 High-level constraints Applications are already scheduled on the machines: not (yet) our job to do it; We want the schedule information distributed over the applis: the goal is not to add a new congestion point; Computing a full I/O schedule over all iterations of all applications would be too expensive (i) in time, (ii) in space. 9
31 High-level constraints Applications are already scheduled on the machines: not (yet) our job to do it; We want the schedule information distributed over the applis: the goal is not to add a new congestion point; Computing a full I/O schedule over all iterations of all applications would be too expensive (i) in time, (ii) in space. We want a minimum overhead for Applis users: otherwise, our guess is, users might not like it that much. 9
32 High-level constraints Applications are already scheduled on the machines: not (yet) our job to do it; We want the schedule information distributed over the applis: the goal is not to add a new congestion point; Computing a full I/O schedule over all iterations of all applications would be too expensive (i) in time, (ii) in space. We want a minimum overhead for Applis users: otherwise, our guess is, users might not like it that much. 9
33 High-level constraints Applications are already scheduled on the machines: not (yet) our job to do it; We want the schedule information distributed over the applis: the goal is not to add a new congestion point; Computing a full I/O schedule over all iterations of all applications would be too expensive (i) in time, (ii) in space. We want a minimum overhead for Applis users: otherwise, our guess is, users might not like it that much. We introduce Periodic Scheduling. 9
34 Periodic schedules Bw c T +c 2T +c 3T +c (n 2)T +c (n 1)T +c nt +c Init Pattern Clean up (a) Periodic schedule (phases) Time Bw B vol (3) io vol (2) vol (4) io vol (2) io vol (2) vol (3) io io io vol (1) io vol (1) io vol (1) io endw (4) 1 initio (4) 1 initw (4) 1 (b) Detail of I/O in a period/pattern T Time 1
35 Periodic schedules Time Schedule vs what Application 4 sees Bw B vol (3) io vol (2) vol (4) io vol (2) io vol (2) vol (3) io io io vol (1) io vol (1) io vol (1) io c endw (4) initw (4) T +c 1 initio (4) 1 1 Time Distributed information Low complexity Minimum overhead 1
36 Periodic schedules Time Schedule vs what Application 4 sees Compute Idle IO + bw Idle IO + bw Compute c endw (4) initw (4) T +c 1 initio (4) 1 1 Time Distributed information Low complexity Minimum overhead 1
37 Finding a schedule Obj: algorithm with good SysEfficiency and Dilation perf. Questions: 1. Pattern length T? 2. How many instances of each application? 3. How to schedule them efficiently? Bw B endw (4) 1 initio (4) 1 initw (4) 1 T Time 11
38 Finding a schedule Obj: algorithm with good SysEfficiency and Dilation perf. Questions: 1. Pattern length T? 2. How many instances of each application? 3. How to schedule them efficiently? Bw B endw (4) 1 initio (4) 1 initw (4) 1 T Time Answers: 1. Iterative search, exponential growth (T min to T max ). 2. Bound on the number of instances of each application O ( ) max k (w (k) +time (k) io ). min k (w (k) +time (k) io ) 3. Greedy insertion of instances, priority to applis with small Dilation. 11
39 Finding a schedule Obj: algorithm with good SysEfficiency and Dilation perf. Questions: 1. Pattern length T? 2. How many instances of each application? Distributed information Low complexity 3. How to schedule them efficiently? Minimum overhead Bw B endw (4) 1 initio (4) 1 initw (4) 1 T Time Answers: 1. Iterative search, exponential growth (T min to T max ). 2. Bound on the number of instances of each application O ( ) max k (w (k) +time (k) io ). min k (w (k) +time (k) io ) 3. Greedy insertion of instances, priority to applis with small Dilation. 11
40 Model validation (I) Announcement: It s hard to find appli data (w (k), vol (k) io, β(k) ). If you have some, let s talk. 12
41 Model validation (I) Four periodic behaviors from the literature to instantiate the applications. Comparison between simulations and a real machine 64 cores, b =.1GB/s, B = 3GB/s). We use IOR benchmark to instantiate the applications on the cluster (ideal world, no other communication than I/O transfers). App (k) w (k) (s) vol (k) io (GB) β (k) Turbulence1 (T1) ,768 Turbulence2 (T2) ,96 AstroPhysics (AP) ,192 PlasmaPhysics (PP) ,768 Set # T1 T2 AP PP
42 Model validation (II) Periodic (expe) Periodic (simu) Online (expe) Online (simu) System Efficiency / Upper bound.8.6 Dilation Periodic (expe) 1.5 Congestion Periodic (simu) Online (expe).4 Online (simu) Congestion (c) SysEfficiency/Upper bound Set Set (d) Dilation The performance estimated by our model is accurate within 3.8% for periodic schedules and 2.3% for online schedules. 13
43 Results Periodic: our periodic algorithm; Online: the best performance (Dilation or SysEff resp.) of any online algorithm in Gainaru et al. Ipdps 15; Congestion: Current performance on the machine. 1. Set Dilation SysEff % % % +7.1% % +8.6% % +1.9% % +.62% 6-2.9% +6.96% % +.73% 8 -.% +.% 9 -.4% +.1% % +.1% System Efficiency / Upper bound Periodic (expe) Periodic (simu) Online (expe) Online (simu) Congestion Set Figure: SysEfficiency/Upper bound 14
44 Results Periodic: our periodic algorithm; Online: the best performance (Dilation or SysEff resp.) of any online algorithm in Gainaru et al. Ipdps 15; Congestion: Current performance on the machine. 3. Periodic (expe) Periodic (simu) Set Dilation SysEff % % % +7.1% % +8.6% % +1.9% % +.62% 6-2.9% +6.96% % +.73% 8 -.% +.% 9 -.4% +.1% % +.1% Dilation Online (expe) Online (simu) Congestion Set Figure: Dilation 14
45 More data We generate more sets of applications System Efficiency / Upper bound Set System Efficiency / Upper bound Periodic Online (best).4 Periodic Online (best) Set 1.6 Simulate on instanciations of Intrepid and Mira. Dilation Periodic Online (best) Dilation 3 2 Periodic Online (best) Set Intrepid Set Mira 15
46 List of open issues / Future steps Study of robustness: what if w (k) and vol (k) io are not exactly known? Integrating non-periodic application Burst-buffers integration/modeling Coupling application scheduler to IO scheduler Evaluation on real applications 16
47 Conclusion Offline periodic scheduling algorithm for periodic applications. Algorithm scales well (complexity depends on the unmber of applications, not on the size of the machine). Very good expected performance. Very precise model on very friendly benchmarks. Right now, more a proof of concept. Paper, data, code:
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