Energy Aware Runtime Systems for Elastic Stream Processing Platforms

Following an invariant growth in the required computational performance of processors, the multicore revolution started around 20 years ago. This revolution was mainly an answer to power dissipation constraints restricting the increase of clock frequency in single-core processors. The multicore revolution not only brought in the challenge of parallel programming, i.e. being able to develop software exploiting the entire capabilities of manycore architectures, but also the challenge of programming heterogeneous platforms. The question of “on which processing element to map a specific computational unit?”, is well known in the embedded community. With the introduction of general-purpose graphics processing units (GPGPUs), digital signal processors (DSPs) along with many-core processors on different system-on-chip platforms, heterogeneous parallel platforms are nowadays widespread over several domains, from consumer devices to media processing platforms for telecom operators. Finding mapping together with a suitable hardware architecture is a process called design-space exploration. This process is very challenging in heterogeneous many-core architectures, which promise to offer benefits in terms of energy efficiency. The main problem is the exponential explosion of space exploration. With the recent trend of increasing levels of heterogeneity in the chip, selecting the parameters to take into account when mapping software to hardware is still an open research topic in the embedded area. For example, the current Linux scheduler has poor performance when mapping tasks to computing elements available in hardware. The only metric considered is CPU workload, which as was shown in recent work does not match true performance demands from the applications. Doing so may produce an incorrect allocation of resources, resulting in a waste of energy. The origin of this research work comes from the observation that these approaches do not provide full support for the dynamic behavior of stream processing applications, especially if these behaviors are established only at runtime. This research will contribute to the general goal of developing energy-efficient solutions to design streaming applications on heterogeneous and parallel hardware platforms. Streaming applications are nowadays widely spread in the software domain. Their distinctive characiteristic is the retrieving of multiple streams of data and the need to process them in real time. The proposed work will develop new approaches to address the challenging problem of efficient runtime coordination of dynamic applications, focusing on energy and performance management.Efter en oföränderlig tillväxt i prestandakrav hos processorer, började den flerkärniga processor-revolutionen för ungefär 20 år sedan. Denna revolution skedde till största del som en lösning till begränsningar i energieffekten allt eftersom klockfrekvensen kontinuerligt höjdes i en-kärniga processorer. Den flerkärniga processor-revolutionen medförde inte enbart utmaningen gällande parallellprogrammering, m.a.o. förmågan att utveckla mjukvara som använder sig av alla delelement i de flerkärniga processorerna, men också utmaningen med programmering av heterogena plattformar. Frågeställningen ”på vilken processorelement skall en viss beräkning utföras?” är väl känt inom ramen för inbyggda datorsystem. Efter introduktionen av grafikprocessorer för allmänna beräkningar (GPGPU), signalprocesserings-processorer (DSP) samt flerkärniga processorer på olika system-on-chip plattformar, är heterogena parallella plattformar idag omfattande inom många domäner, från konsumtionsartiklar till mediaprocesseringsplattformar för telekommunikationsoperatörer. Processen att placera beräkningarna på en passande hårdvaruplattform kallas för utforskning av en designrymd (design-space exploration). Denna process är mycket utmanande för heterogena flerkärniga arkitekturer, och kan medföra fördelar när det gäller energieffektivitet. Det största problemet är att de olika valmöjligheterna i designrymden kan växa exponentiellt. Enligt den nuvarande trenden som förespår ökad heterogeniska aspekter i processorerna är utmaningen att hitta den mest passande placeringen av beräkningarna på hårdvaran ännu en forskningsfråga inom ramen för inbyggda datorsystem. Till exempel, den nuvarande schemaläggaren i Linux operativsystemet är inkapabel att hitta en effektiv placering av beräkningarna på den underliggande hårdvaran. Det enda mätsättet som används är processorns belastning vilket, som visats i tidigare forskning, inte motsvarar den verkliga prestandan i applikationen. Användning av detta mätsätt vid resursallokering resulterar i slöseri med energi. Denna forskning härstammar från observationerna att dessa tillvägagångssätt inte stöder det dynamiska beteendet hos ström-processeringsapplikationer (stream processing applications), speciellt om beteendena bara etableras vid körtid. Denna forskning kontribuerar till det allmänna målet att utveckla energieffektiva lösningar för ström-applikationer (streaming applications) på heterogena flerkärniga hårdvaruplattformar. Ström-applikationer är numera mycket vanliga i mjukvarudomän. Deras distinkta karaktär är inläsning av flertalet dataströmmar, och behov av att processera dem i realtid. Arbetet i denna forskning understöder utvecklingen av nya sätt för att lösa det utmanade problemet att effektivt koordinera dynamiska applikationer i realtid och fokus på energi- och prestandahantering

A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided

Accelerating Real-Time, High-Resolution Depth Upsampling on FPGAs

While the popularity of high-resolution, computer-vision applications (e.g. mixed reality, autonomous vehicles) is increasing, there have been complementary advances in time-of-flight (ToF) depth-sensor resolution and quality. These advances in ToF sensors provide a platform that can enable real-time, depth-upsampling algorithms targeted for high-resolution video systems with low-latency requirements. This thesis demonstrates that filter-based upsampling algorithms are feasible for real-time, low-power scenarios, such as those on HMDs. Specifically, the author profiled, parallelized, and accelerated a filter-based depth-upsampling algorithm on an FPGA using high-level synthesis tools from Xilinx. We show that our accelerated algorithm can accurately upsample the resolution and reduce the noise of ToF sensors. We also demonstrate that this algorithm exceeds the real-time requirements of 90 frames-per-second (FPS) and 11 ms latency of mixed-reality hardware, achieving a lower-bound speedup of 40 times over the fastest CPU-only version and a 4.7 times speedup over the original GPU implementation