Pm-gawk

2022

1. MICRO

   AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server Applications

   Jawad Haj-Yahya, Haris Volos, Davide B. Bartolini, Georgia Antoniou, Jeremie S. Kim, Zhe Wang, Kleovoulos Kalaitzidis, Tom Rollet, Zhirui Chen, Ye Geng, Onur Mutlu, and Yiannakis Sazeides

   In MICRO ’22: Proceedings of the 55th IEEE/ACM International Symposium on Microarchitecture 2022

   Abs DOI

   User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30–250\mus). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100\mus) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive applications.AW drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of savinglrestoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) reraining context in the power-ungated domain. Second, AW eliminates rhe flush latency (several tens of microseconds) of the LllL2 caches when entering a deep idle state by keeping LllL2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core’s voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power cost.Our evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumprion of Memcached by up to 71% (35% on average) with<1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka). AW’s new deep C-states C6A and C6AE reduce transition-time by up to 900x as compared to the deepest existing idle state C6, while consuming only 7% and 5% of the active state (C0) power, respectively.

2. MICRO

   AgilePkgC: An Agile System Idle State Architecture for Energy Proportional Datacenter Servers

   Georgia Antoniou, Haris Volos, Davide B. Bartolini, Tom Rollet, Yiannakis Sazeides, and Jawad Haj-Yahya

   In MICRO ’22: Proceedings of the 55th IEEE/ACM International Symposium on Microarchitecture 2022

   Abs DOI

   Modern user-facing applications deployed in datacenters use a distributed system architecture that exacerbates the latency requirements of their constituent microservices (30-250\mus). Existing CPU power-saving techniques degrade the performance of these applications due to the long transition latency (order of 100\mus) to wake up from a deep CPU idle state (C-state). For this reason, server vendors recommend only enabling shallow core C-states (e.g., CC1) for idle CPU cores, thus preventing the system from entering deep package C-states (e.g., PC6) when all CPU cores are idle. This choice, however, impairs server energy proportionality since power-hungry resources (e.g., IOs, uncore, DRAM) remain active even when there is no active core to use them. As we show, it is common for all cores to be idle due to the low average utilization (e.g., 5-20%) of datacenter servers running user-facing applications. We propose to reap this opportunity with AgilePkgC (APC), a new package C-state architecture that improves the energy proportionality of server processors running latency-critical applications. APC implements PC 1A (package C l agile), a new deep package C-state that a system can enter once all cores are in a shallow C-state (i.e., CC1) and has a nanosecond-scale transition latency. PC 1A is based on four key techniques. First, a hardware-based agile power management unit (APMU) rapidly detects when all cores enter a shallow core C-state (CC1) and triggers the system-level power savings control flow. Second, an IO Standby Mode (IOSM) places IO interfaces (e.g., PCIe, DMI, UPI, DRAM) in shallow (nanosecond-scale transition latency) low-power modes. Third, a CLM Retention (CLMR) mode rapidly reduces the CLM (Cache-and-home-agent, Last-level-cache, and Mesh network-on-chip) domain’s voltage to its retention level, drastically reducing its power consumption. Fourth, APC keeps all system PLLs active in PC 1A to allow nanosecond-scale exit latency by avoiding PLL re-locking overhead. Combining these techniques enables significant power savings while requiring less than 200ns transition latency, \gt250\times faster than existing deep package C-states (e.g., PC6), making PC 1A practical for datacenter servers. Our evaluation based on an Intel Skylake-based server shows that APC reduces the energy consumption of Memcached by up to 41% (25% on average) with <0.1% performance degradation. APC provides similar benefits for other representative workloads.

3. NVMW

   Persistent Scripting

   Zi Fan Tan, Jianan Li, Haris Volos, and Terence Kelly

   In NVMW ’22: 13th Annual Non-Volatile Memories Workshop 2022

   PDF Media Code
4. ACM Queue

   Persistent Memory Allocation: Leverage to move a world of software

   Terence Kelly, Zi Fan Tan, Jianan Li, and Haris Volos

   ACM Queue magazine 2022

   Abs DOI HTML Media

   A lever multiplies the force of a light touch, and the right software interfaces provide formidable leverage in multiple layers of code: A familiar interface enables a new persistent memory allocator to breathe new life into an enormous installed base of software and hardware. Compatibility allows a persistent heap to slide easily beneath a widely used scripting-language interpreter, thereby endowing all scripts with effortless on-demand persistence.

5. TOCS

   Unified Holistic Memory Management Supporting Multiple Big Data Processing Frameworks over Hybrid Memories

   Lei Chen, Jiacheng Zhao, Chenxi Wang, Ting Cao, John Zigman, Haris Volos, Onur Mutlu, Fang Lv, Xiaobing Feng, Guoqing Harry Xu, and Huimin Cui

   ACM Transactions on Computer Systems 2022

   Abs DOI

   To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy-inefficient. Emerging non-volatile memory (NVM) technologies offer high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in managed languages and executed on top of a managed runtime that already performs various dimensions of memory management. Supporting hybrid physical memories adds in a new dimension, creating unique challenges in data replacement. This paper proposes Panthera, a semantics-aware, fully automated memory management technique for Big Data processing over hybrid memories. Panthera analyzes user programs on a Big Data system to infer their coarse-grained access patterns, which are then passed to the Panthera runtime for efficient data placement and migration. For Big Data applications, the coarse-grained data division information is accurate enough to guide the GC for data layout, which hardly incurs overhead in data monitoring and moving. We implemented Panthera in OpenJDK and Apache Spark. Based on Big Data applications’ memory access pattern, we also implemented a new profiling-guided optimization strategy, which is transparent to applications. With this optimization, our extensive evaluation demonstrates that Panthera reduces energy by 32 – 53% at less than 1% time overhead on average. To show Panthera’s applicability, we extend it to QuickCached, a pure Java implementation of Memcached. Our evaluation results show that Panthera reduces energy by 28.7% at 5.2% time overhead on average.