This paper compares the energy efficiency of chip multiprocessing (CMP) and simultaneous multithreading (SMT) on modern out-of-order processors for the increasingly important multimedia applications. Since performance is an important metric for realtime multimedia applications, we compare configurations at equal performance. We perform this comparison for a large number of performance points derived using different processor architectures and frequencies/voltages. We find that for the design space explored, for each workload, at each performance point, CMP is more energy efficient than SMT. The difference is small for two thread systems, but large (18% to 44%) for four thread systems. We also find that the best SMT and the best CMP configuration for a given performance target have different architecture and frequency/voltage. Therefore, their relative energy efficiency depends on a subtle interplay between various factors such as capacitance, voltage, IPC, frequency, and the level of clock gating, as well as workload features. We perform a detailed analysis considering these factors and develop a mathematical model to explain these results.Although CMP shows a clear energy advantage for four-thread (and higher) workloads, it comes at the cost of increased silicon area. We therefore investigate a hybrid solution where a CMP is built out of SMT cores, and find it to be an effective compromise. Finally, we find that we can reduce energy further for CMP with a straightforward application of previously proposed techniques of adaptive architectures and dynamic voltage/frequency scaling.

International audienceIn current computer architectures, data movement (from die to network) is by far the most energy consuming part of an algorithm (View the MathML source≈20pJ/word on-die to ≈10,000 pJ/word≈10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction registers that are able to process multiple data with one arithmetic operator in one clock cycle. SIMD register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide data registers). Results show a factor of ×2×2 to ×2.5×2.5 speed-up in double precision for particle shape factor of orders 11–33. The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits register lengths (8 doubles/16 singles)

This work concerns algorithms to control energy-driven architecture adaptations for multimedia applications, without and with dynamic voltage scaling (DVS). We identify a broad design space for adaptation control algorithms based on two attributes: (1) when to adapt or temporal granularity and (2) what structures to adapt or spatial granularity. For each attribute, adaptation may beglobal or local. Our previous work developed a temporally and spatially global algorithm. It invokes adaptation at the granularity of a full frame of a multimedia application (temporally global) and considers the entire hardware configuration at a time (spatially global). It exploits inter-frame execution time variability, slowing computation just enough to eliminate idle time before the real-time deadline. This paper explores temporally and spatially local algorithms and their integration with the previous global algorithm. The local algorithms invoke architectural adaptation within an application frame to exploit intra-frame execution variability, and attempt to save energy without affecting execution time. We consider local algorithms previously studied for non-real-time applications as well as propose new algorithms. We find that, for systems without and with DVS, the local algorithms are effective in saving energy for multimedia applications, but the new integrated global and local algorithm is best for the systems and applications studied.

This work concerns algorithms to control energy-driven architecture adaptations for multimedia applications, without and with dynamic voltage scaling (DVS). We identify a broad design space for adaptation control algorithms based on two attributes: (1) when to adapt or temporal granularity and (2) what structures to adapt or spatial granularity. For each attribute, adaptation may beglobal or local. Our previous work developed a temporally and spatially global algorithm. It invokes adaptation at the granularity of a full frame of a multimedia application (temporally global) and considers the entire hardware configuration at a time (spatially global). It exploits inter-frame execution time variability, slowing computation just enough to eliminate idle time before the real-time deadline. This paper explores temporally and spatially local algorithms and their integration with the previous global algorithm. The local algorithms invoke architectural adaptation within an application frame to exploit intra-frame execution variability, and attempt to save energy without affecting execution time. We consider local algorithms previously studied for non-real-time applications as well as propose new algorithms. We find that, for systems without and with DVS, the local algorithms are effective in saving energy for multimedia applications, but the new integrated global and local algorithm is best for the systems and applications studied.

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