Self-Aware Adaptation in FPGA-based Systems
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1 DIPARTIMENTO DI ELETTRONICA E INFORMAZIONE Self-Aware Adaptation in FPGA-based Systems IEEE FPL 2010 Filippo Siorni: filippo.sironi@dresd.org Marco Triverio: marco.triverio@dresd.org Martina Maggio: mmaggio@mit.edu Hank Hoffmann: hank@csail.mit.edu Marco D. Santambrogio: santa@csail.mit.edu August 31 September 2, 2010 Politecnico di Milano Milano, Italy
2 Rationale and Contribution Modern computing systems complexity is skyrocketing mainly due to:! Tremendous availability and integration of heterogeneous resources! Demand for performance and reliability We present a Self-Aware Adaptive computing system blending techniques from different research fields to exploit available resources considering the execution context 2
3 Motivation example Thanks to the availability of heterogeneous resources, modern computing systems provide the ability to provide many different implementation of a functionality When multiple implementations of a functionality are available, taking reasonable choices at compile time about which one better suits the execution context is not a trivial task 3
4 Outline Introduction on Self-Aware Adaptive computing systems Context definition Proposed approach to implement Self- Aware Adaptive computing systems! Observe! Decide! Act Experimental results Conclusions and Future works 4
5 Introduction (1 of 2) The key idea: manage technology and its complexity using technology itself changing the computing system behavior and resources management policies The key characteristics:! Awareness! Adaptation! Approximation! Goal-orientation 5
6 Introduction (2 of 2) Self-Aware Adaptive computing systems follow the Observe, Decide, and Act loop: Input Goal Decide Environment Internals Observe Act Output State 6
7 Context definition (1 of 2) The literature is filled with works taking advantage of online monitoring/profiling, decision making, or hardware/software partitioning either static (compile time defined) or dynamic (run time defined) Many of these works does not exploit all these features resulting in a lack of Self-Aware Adaptation 7
8 Context definition (2 of 2) The use of static hardware/software partitioning to select an implementation of functionality prevents any kind of Self- Adaptation The use of dynamic hardware/software partitioning allows Self-Adaptation on a degree depending on Self-Awareness which depends on online monitoring/ profiling 8
9 Proposed approach The Self-Aware Adaptive computing system we have developed fully exploit the ODA-loop to overcome some of the limits we outlined in state of the art solutions The ODA-loop is implemented by means of three sub-systems:! Heartbeats: observe! Heuristic decision making process: decide! Implementation Switch Service: act 9
10 Observe Online performance assertion and monitoring are performed by means of Heartbeats Heartbeats is simple yet extremely powerful library used to declare performance goals and to update and monitor the overall throughput of an application It is based on two simple concepts:! Heartbeat! Heart rate More information at: and 10
11 Decide The decision making process can be implemented in many different ways:! Heuristic methods! Probabilistic methods! Machine learning techniques (1)! Control theory techniques (2) We implement a heuristic that uses information collected by Heartbeats and avoids oscillations (1) J. Eastep, et al.: Smartlocks: Lock Acquisition Scheduling for Self-Aware Synchronization (ICAC 10) (2) M. Maggio, et al.: Controlling software applications via resource allocation within the Heartbeats framework (to appear in CDC '10) 11
12 Act (1 of 2) The ability to hot-swap an implementation of a given functionality in favor of another one proves to be a fundamental characteristic for a Self- Aware Adaptive computing system The Implementation Switch Service behavior has been inspired by the hotswap mechanism available in K42 J. Appavoo, et al.: Experience with K42, an open-source, Linux-compatible, scalable operating-system kernel (IBM Systems Journal 05) 12
13 Act (2 of 2) Switchable unit: Dynamic-Link Library State translation: on-the-fly translation of the canonical data structure Self-aware adaptive library Data structure Self-aware adaptive library Data structure Canonical data structure Software implementation Hardware implementation Canonical data structure Software implementation Hardware implementation Data structure Data structure 13
14 Experimental results Testing platform:! Xilinx XC2VP30 FPGA, IBM PowerPC 405, 256 MB of SDRAM, and 1 GB of Flash running a Linux-based operating system Static analysis of both the hardware and the software implementation of the DES cryptographic algorithm Dynamic analysis of the Self-Aware Adaptive computing system Online monitoring, decision making process, and dynamic reconfiguration overheads analysis 14
15 Static analysis 1000! Software! Hardware! Reconfigurable Hardware! 900! 800! Execution time [ms]! 700! 600! 500! 400! 300! 200! 100! 0! 1! 2! 3! 4! 5! 6! 7! 8! 9! 10! 20! 30! 40! 50! 60! 70! 80! 90! 100! 200! 300! 400! 500! 600! 700! 800! 900! 1000! Blocks [#]! 15
16 Dynamic analysis M Δ observation delta R reconfiguration time Δ m minimum heart rate M maximum heart rate Δ Heart rate m R t0 t1 t2 t3 t4 t5 t6 t7 Time 16
17 Overheads (1 of 2) 7,00%! Overhead! Average! 6,00%! Overhead on Execution time! 5,00%! 4,00%! 3,00%! 2,00%! 1,00%! 0,00%! 1! 2! 3! 4! 5! 6! 7! 8! 9! 10! 20! 30! 40! 50! 60! 70! 80! 90! Blocks [#]! 100! 200! 300! 400! 500! 600! 700! 800! 900! 1000! 3,52%! 17
18 Overheads (2 of 2) 95,50%! Hardware! Reconfigurable Hardware! Overhead! Average Overhead! 200! 180! Overhead on Execution time! 95,00%! 94,50%! 94,00%! 93,50%! 93,00%! 92,50%! 1! 2! 3! 4! 5! 6! 7! 8! 9! 10! 20! 30! 40! 50! 60! 70! 80! 90! Blocks [#]! 100! 200! 300! 400! 500! 600! 700! 800! 900! 1000! 93,87%! 160! 140! 120! 100! 80! 60! 40! 20! 0! Execution times [ms]! 18
19 Conclusions The proposed approach merges the potential of reconfigurable architectures with online performance assertion and monitoring, and adaptation capabilities Experimental results show the goodness of the proposed approach when the computing system works within an unpredictable environment The overhead of the online monitoring and decision making process proved to be sustainable 19
20 Future works Run the Linux-based operating system on top of a multi-core processor sided with an FPGA used as an accelerator co-processor Implement an hot-swap mechanism within the Linux kernel to allow the implementation switch between device drivers optimized for different execution contexts 20
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