Next Generation Platforms for Data Intensive Applications. Ian Gray, Neil Audsley

Transcription

1 Next Generation Platforms for Data Intensive Applications Ian Gray, Neil Audsley

2 Introduction Data-intensive applications have many more requirements than simply the amount of data that they can process Timing Security Geographically distinct heterogeneous deployment Custom hardware such as GPUs or FPGAs Current solutions Hadoop, Spark, Storm, Docker,Vagrant, Kubernetes Require platform support and programming models Fine-grained containerisation and micro-services Platform support for deployment 2

3 The JUNIPER Project Java platform for high performance and real-time large scale data management Ahead-of-time guarantees in a big-data environment Predictability-focussed Predictable OS and JVM Provisioning of network and disk bandwidth Integration of FPGA translation Automatic mapping support 3

4 Locality in JUNIPER Problem: In the cloud or HPC your code may run anywhere Platform for pessimism reduction JUNIPER s programming model provides Locales Collections of threads and data which inform the platform The platform uses a Scheduling Advisor to deploy Real-time OS JUNIPER platform JUNIPER programming model (Locales, Requirements) Real-time JVM Locales define their timing and bandwidth requirements Disk and bandwidth scheduling In-cloud Scheduling Advisor FPGA translation Locales are ideal for FPGA translation FPGA comms 4

5 Model Driven Engineering Allows specification of real-time requirements Allow large scale portable application deployment Extensive use of code generation to speed development Challenges in Data-Centric Computing

6 Model Driven public class ConsumerProgram public static final int RANK = ("4db8f2b4-6aed e4a-18e678a69178") public static DataConnection dataconnectionimpl = new DataConnection() public static void initprovidedinterfaces() { Util.initProvidedInterface( ProducerProgram.class, dataconnectionimpl); public static void main(final String[] args) { MPI.Init(args); initprovidedinterfaces(); while (true) { Thread.yield(); Util.processReceivedMessages(); if (execute()) break; } MPI.Finalize(); } //...Further detail omitted Challenges in Data-Centric Computing

7 Locales Manually created, contain threads and data API can assign locales to patterns Threads will be scheduled in that pattern, data will be allocated in its memory Provides portable locality without onerous work Platform p = Platform.getPlatform(); NUMA numa = p.getrootlocation(); CCNUMA ccnuma = numa.getchildren()[0]; SMP smp = ccnuma.getchildren()[0]; Locale locale = new Locale(smp); int ncpu = smp.getnumcpus(); for (int i = 0; i < ncpu; i++) { Thread th = locale.createjavathread(() -> { //... }); th.start(); } Challenges in Data-Centric Computing

8 FPGA Acceleration Challenges in Data-Centric Computing

9 Predictability Histograms of execution times for FFT core Java on standard OS FPGA implementation " " 10000" 10000" Frequency) 1000" 100" Frequency) 1000" 100" 10" 10" 1" 0" 5" 10" 15" 20" 25" 30" 35" 40" Execu,on),me)(ms)) 1" 150" 170" 190" 210" 230" 250" 270" 290" 310" Execu,on),me)(clocks)) Worst-case variance is far greater on a general purpose machine SD = , vs 13.4 on the FPGA 9

10 Kernel and OS support JUNIPER s Java libraries access the JFIM kernel module FPGA configuration DMA Bandwidth allocation Communications are handled in the MPI library 10

11 Validation JUNIPER has been validated in the financial and web domains to create interactive applications Fraud detection systems From a periodic batch job, to <4 second live results Particular praise of the integrated platform and MDE Allows very rapid development 11

12 The PHANTOM Project A platform for the computing continuum Telecommunications, Space, & HPC use cases Highly variable deployment architectures Some very strict non-functional requirements 12

13 The PHANTOM Project Integrates automatic mapping, parallelisation, and testing to support non-functional requirements Component-based Programming Model Parallelisation Toolset Component Repository Multiobjective Mapper Model- Based Testing Deployment PHANTOM Platform Monitoring Security 13

14 The PHANTOM Project A PHANTOM application is a set of parallel components (micro-services) Lower level than a Docker container! Normal C/C++ programs No implicit data sharing Components use protocols to define shared data Define security and isolation requirements Read Component B Push Application topology is static (deployment may be dynamic) Component A Read / Write Shared Memory Queue The MOM places these components and their data in the system Write Pop Component C 14

15 The PHANTOM Project //Component A #pragma phantom shared out the_byte uint8_t byte; int main() {... byte = 100; //Write the shared byte... } uint8_t the_byte //Component B #pragma phantom shared in the_byte uint8_t x; int main() {... printf("%d", x); //Read the shared byte... } 15

16 PHANTOM Progress PHANTOM is ongoing work Lots of implementation yet to do! 16

17 Conclusions Containerised programming models are a useful abstraction But lower-levels are required to achieve challenging NFPs Platforms have to support the issues of security, timing, testing Automatic mapping is key 17

18 Thanks 18

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