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Section: Research Program
Research Objectives
Processor micro-architecture and compilation have been at the core of the research carried by the CAPS and ALF project teams for two decades, with undeniable contributions. They will continue to be the foundation of PACAP.
Heterogeneity and diversity of processor architectures now require new techniques to guarantee that the hardware is satisfactorily exploited by the software. We will devise new static compilation techniques (cf. Section 3.2.1), but also build upon iterative [1] and split [2] compilation to continuously adapt software to its environment (Section 3.2.2). Dynamic binary optimization will also play a key role in delivering adapting software and delivering performance.
The end of Moore's law and Dennard's scaling (According to Dennard scaling, as transistors get smaller the power density remains constant, and the consumed power remains proportional to the area.) offer an exciting window of opportunity, where performance improvements will no longer derive from additional transistor budget or increased clock frequency, but rather come from breakthroughs in microarchitecture (Section 3.2.3). We will also consider how to reconcile CPU and GPU designs (Section 3.2.4).
Heterogeneity and multicores are also major obstacles to determining tight worst-case execution times of real-time systems (Section 3.2.5), which we plan to tackle.
Finally, we also describe how we plan to address transversal aspects such reliability (Section 3.2.6), power efficiency (Section 3.2.7), and security (Section 3.2.8).
Static Compilation
Static compilation techniques will continue to be relevant to address the characteristics of emerging hardware technologies, such as non-volatile memories, 3D-stacking, or novel communication technologies. These techniques expose new characteristics to the software layers. As an example, non-volatile memories typically have asymmetric read-write latencies (writes are much longer than reads) and different power consumption profiles. PACAP will study the new optimization opportunities and develop tailored compilation techniques for the upcoming compute nodes. New technologies may also be coupled with traditional solutions to offer new trade-offs. We will study how programs can adequately exploit the specific features of the proposed heterogeneous compute nodes.
We propose to build upon iterative compilation [1] to explore how applications perform on different configurations. When possible, Pareto points will be related to application characteristics. The best configuration, however, may actually depend on runtime information, such as input data, dynamic events, or properties that are available only at runtime. Unfortunately a runtime system has little time and means to determine the best configuration. For these reasons, we will also leverage split-compilation [2]: the idea consists in pre-computing alternatives, and embedding in the program enough information to assist and drive a runtime system towards to the best solution.
Software Adaptation
More than ever, software will need to adapt to their environment. In most cases, this environment will remain unknown until runtime. This is already the case when one deploys an application to a cloud, or an “app” to mobile devices. The dilemma is the following: for maximum portability, developers should target the most general device; but for performance they would like to exploit the most recent and advanced hardware features. JIT compilers can handle the situation to some extent, but binary deployment requires dynamic binary rewriting. Our work has shown how SIMD instructions can be upgraded from SSE to AVX [3]. Many more opportunities will appear with diverse and heterogeneous processors, featuring various kinds of accelerators.
On shared hardware, the environment is also defined by other applications competing for the same computational resources. It will become increasingly important to adapt to changing runtime conditions, such as the contention of the cache memories, available bandwidth, or hardware faults. Fortunately, optimizing at runtime is also an opportunity, because this is the first time the program is visible as a whole: executable and libraries (including library versions). Optimizers may also rely on dynamic information, such as actual input data, parameter values, etc. We have already developed a software platform [14] to analyze and optimize programs at runtime, and we started working on automatic dynamic parallelization of sequential code, and dynamic specialization.
We started addressing some of these challenges in ongoing projects such as Nano2017 PSAIC Collaborative research program with STMicroelectronics, as well as within the Inria Project Lab MULTICORE. The starting H2020 FET HPC project ANTAREX will also address these challenges from the energy perspective. We will further leverage our platform and initial results to address other adaptation opportunities. Efficient software adaptation will require expertise from all domains tackled by PACAP, and strong interaction between all team members is expected.
Research directions in uniprocessor microarchitecture
Achieving high single-thread performance remains a major challenge even in the multicore era (Amdahl's law). The members of the PACAP project-team have been conducting research in uniprocessor micro-architecture research for about 20 years covering major topics including caches, instruction front-end, branch prediction, out-of-order core pipeline, branch prediction and value prediction. In particular, in the recent years they have been recognized world leaders in branch prediction [19][9] and in cache prefetching [7] and they have revived the forgotten concept of value prediction [12], [11]. This research was supported by the ERC Advanced grant DAL (2011-2016) and also by Intel. We intend to pursue research on achieving ultimate unicore performance. Below are several non-orthogonal directions that we have identified for mid-term research:
Memory design issues:
Performance of many applications is highly impacted by the memory hierarchy behavior. The interactions between the different components in the memory hierarchy and the out-of-order execution engine have high impact on performance.
The last Data Prefetching Contest held with ISCA 2015 has illustrated that achieving high prefetching efficiency is still a challenge for wide-issue superscalar processors, particularly those featuring a very large instruction window. The large instruction window enables an implicit data prefetcher. The interaction between this implicit hardware prefetcher and the explicit hardware prefetcher is still relatively mysterious as illustrated by Pierre Michaud's BO prefetcher (winner of DPC2) [7]. The first objective of the research is to better understand how the implicit prefetching enabled by the large instruction window interacts with the L2 prefetcher and then to understand how explicit prefetching on the L1 also interacts with the L2 prefetcher.
The second objective of the research is related to the interaction of prefetching and virtual/physical memory. On real hardware, prefetching is stopped by page frontiers. The interaction between TLB prefetching (and on which level) and cache prefetching must be analyzed.
The prefetcher is not the only actor in the hierarchy that must be carefully controlled. Significant benefit can also be achieved through careful management of memory access bandwidth, particularly the management of spatial locality on memory accesses, both for reads and writes. The exploitation of this locality is traditionally handled in the memory controller. However, it could be better handled if larger temporal granularity was available. Finally, we also intend to continue to explore the promising avenue of compressed caches. In particular we recently proposed the skewed compressed cache [15]. It offers new possibility for efficient compression schemes.
Ultra wide-issue superscalar.
To effectively leverage memory level parallelism, one requires huge out-of-order execution structures as well as very wide issue superscalar processor. For the two past decades, implementing always wider issue superscalar processor has been challenging. The objective of our research on the execution core is to explore (and revisit) directions to allow the design of a very wide-issue (8-to-16 way) out-of-order execution core while mastering its complexity (silicon area, hardware logic complexity, power/energy consumption).
The first direction that we intend to explore is the use of clustered architecture as in our recent work [8]. Symmetric clustered organization allows to benefit from simpler bypass network, but induce large complexity on the issue queue. One remarkable finding of our study [8] is that, when considering two large clusters (e.g. 8-wide) steering large groups of consecutive instructions (e.g. 64 ops) to the same cluster is quite efficient. This opens opportunities to limit the complexity of the issue queues (monitoring less fewer buses) and register files (reducing number of ports, and number of physical registers) in the clusters, since not all results have to be forwarded to the other cluster.
The second direction that we intend to explore is associated with the approach we developed with Sembrant et al. [16]. It reduces the number of instructions waiting in the instruction queues for the applications benefiting from very large instruction windows. Instructions are dynamically classified as ready (independent from any long latency instruction) or non-ready, and as urgent (part of a dependency chain leading to a long latency instruction) or non-urgent. Non-ready non-urgent instructions can be delayed until the long latency instruction has been executed; this allows to reduce the pressure on the issue queue. This proposition opens the opportunity to consider an asymmetric microarchitecture with a cluster dedicated to the execution of urgent instructions and a second cluster executing the non-urgent instructions. The microarchitecture of this second cluster could be optimized to reduce complexity and power consumption (smaller instruction queue, less aggressive scheduling...)
Speculative execution.
Out-of-order (OoO) execution relies on speculative execution that requires predictions of all sorts: branch, memory dependency, value...
The PACAP members have been major actors of the branch prediction research for the last 20 years; and their proposals have influenced the design of most of the hardware branch predictors in current microprocessors. We will continue to steadily explore new branch predictor designs as for instance [18].
In speculative execution, we have recently revisited value prediction (VP) which was a hot research topic between 1996 and 2002. However it was considered up to recently that value prediction would lead to a huge increase in complexity and power consumption in every stage of the pipeline. Fortunately, we have recently shown that complexity usually introduced by value prediction in the OoO engine can be overcome [12], [11], [19], [9]. First, very high accuracy can be enforced at reasonable cost in coverage and minimal complexity [12]. Thus, both prediction validation and recovery by squashing can be done outside the out-of-order engine, at commit time. Furthermore, we propose a new pipeline organization, EOLE ({Early | Out-of-order | Late} Execution), that leverages VP with validation at commit to execute many instructions outside the OoO core, in-order [11]. With EOLE, the issue-width in OoO core can be reduced without sacrificing performance, thus benefiting the performance of VP without a significant cost in silicon area and/or energy. In the near future, we will explore new avenues related to value prediction. These directions include register equality prediction and compatibility of value prediction with weak memory models in multiprocessors.
Towards heterogeneous single-ISA CPU-GPU architectures
Heterogeneous single-ISA architectures have been proposed in the literature during the 2000's [56] and are now widely used in the industry (ARM big.LITTLE, NVIDIA 4+1...) as a way to improve power-efficiency in mobile processors. These architectures include multiple cores whose respective microarchitectures offer different trade-offs between performance and energy efficiency, or between latency and throughput, while offering the same interface to software. Dynamic task migration policies leverage the heterogeneity of the platform by using the most suitable core for each application, or even each phase of processing. However, these works only tune cores by changing their complexity. Energy-optimized cores are either identical cores implemented in a low-power process technology, or simplified in-order superscalar cores, which are far from state-of-the-art throughput-oriented architectures such as GPUs.
We propose to investigate the convergence of CPU and GPU at both architecture and compilation levels.
Architecture.
The architecture convergence between Single Instruction Multiple Threads (SIMT) GPUs and multicore processors that we have been pursuing [36] opens the way for heterogeneous architectures including latency-optimized superscalar cores and throughput-optimized GPU-style cores, which all share the same instruction set. Using SIMT cores in place of superscalar cores will enable the highest energy efficiency on regular sections of applications. As with existing single-ISA heterogeneous architectures, task migration will not necessitate any software rewrite and will accelerate existing applications.
Compilers for emerging heterogeneous architectures.
Single-ISA CPU+GPU architectures will provide the necessary substrate to enable efficient heterogeneous processing. However, it will also introduce substantial challenges at the software and firmware level. Task placement and migration will require advanced policies that leverage both static information at compile time and dynamic information at run-time. We are tackling the heterogeneous task scheduling problem at the compiler level. As a first step, we are prototyping scheduling algorithms on existing multiple-ISA CPU+GPU architectures like NVIDIA Tegra X1.
Real-time systems
Safety-critical systems (e.g. avionics, medical devices, automotive...) have so far used simple unicore hardware systems as a way to control their predictability, in order to meet timing constraints. Still, many critical embedded systems have increasing demand in computing power, and simple unicore processors are not sufficient anymore. General-purpose multicore processors are not suitable for safety-critical real-time systems, because they include complex micro-architectural elements (cache hierarchies, branch, stride and value predictors) meant to improve average-case performance, and for which worst-case performance is difficult to predict. The prerequisite for calculating tight WCET is a deterministic hardware system that avoids dynamic, time-unpredictable calculations at run-time.
Even for multi and manycore systems designed with time-predictability in mind (Kalray MPPA manycore architecture (http://www.kalrayinc.com), or the Recore manycore hardware (http://www.recoresystems.com/)) calculating WCETs is still challenging. The following two challenges will be addressed in the mid-term:
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definition of methods to estimate WCETs tightly on manycores, that smartly analyzes and/or controls shared resources such as buses, NoCs or caches;
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methods to improve the programmability of real-time applications through automatic parallelization and optimizations from model-based designs.
Fault Tolerance
Technology trends suggest that, in tomorrow's computing world, failures will become commonplace due to many factors, and the expected probability of failure will increase with scaling. While well-known approaches, such as error correcting codes, exist to recover from failures and provide fault-free chips, the exponential growth of the number of faults will make them unaffordable in the future. Consequently, other approaches such as fine-grained disabling and reconfiguration of hardware elements (e.g. individual functional units or cache blocks) will become economically necessary. We are going to enter a new era: functionally correct chips with variable performance among chips and throughout their lifetime [58].
Transient and permanent faults may be detected by similar techniques, but correcting them generally involves different approaches. We are primarily interested in permanent faults, even though we do not necessarily disregard transient faults (e.g. the TMR approach in the next paragraph addresses both kind of faults).
CPU.
Permanent faults can occur anywhere in the processor. The performance implications of faulty cells vary depending on how the array is used in a processor. Most of micro-architectural work aiming at assessing the performance implications of permanently faulty cells relies on simulations with random fault-maps. These studies are, therefore, limited by the fault-maps they use that may not be representative for the average and distributed performance. They also do not consider aging effect.
Considering the memory hierarchy, we have already studied [5] the impact of permanent faults on the average and worst-case performance based on analytical models. We will extend these models to cover other components and other designs, and to analyze the interaction between faulty components.
For identified critical hardware structures, such as the memory hierarchy, we will propose protection mechanisms by for instance using larger cells, or even by selecting a different array organization to mitigate the impact of faults.
Another approach to deal with faults is to introduce redundancy at the code level. We propose to consider static compilation techniques focusing on existing hardware. As an example, we plan to leverage SIMD extensions of current instruction sets to introduce redundancy in scalar code at minimum cost. With these instructions, it will be possible to protect the execution from both soft errors by using TMR (triple modular redundancy) with voters in the code itself, and permanent faults without the need of extra hardware support to deconfigure faulty functional units.
Reconfigurable Computing.
In collaboration with the Cairn project-team, we propose to construct Coarse Grain Reconfigurable Architectures (CGRA) from a sea of basic arithmetic and memory elements organized into clusters and connected through a hierarchical interconnection network. These clusters of basic arithmetic operators (e.g. 8-bit arithmetic and logic units) would be able to be seamlessly configured to various accuracy and data types to adapt the consumed energy to application requirements taking advantage of approximate computations. We propose to add new kinds of error detection (and sometimes correction) directly at the operator level by taking advantage of the massive redundancy of the array. As an example, errors can be tracked and detected in a complex sequence of double floating-point operations by using a reduced-precision version of the same processing.
Such reconfigurable blocks will be driven by compilation techniques, in charge of computing checkpoints, detecting faults, and replaying computations when needed.
Dynamic compilation techniques will help better exploit faulty hardware, by allocating data and computations on correct resources. In case of permanent faults, we will provide a mechanism to reconfigure the hardware, for example by reducing the issue width of VLIW processors implemented in CGRA. Dynamic code generation (JIT compiler) will re-generate code for the new configuration, guaranteeing portability and optimal exploitation of the hardware.
Power efficiency
PACAP will address power-efficiency at several levels. First, we will design static and split compilation techniques to contribute to the race for Exascale computing (the general goal is to reach FLOP/s at less than 20 MW). Second, we will focus on high-performance low-power embedded compute nodes. will research new static and dynamic compilation techniques that fully exploit emerging memory and NoC technologies. Finally, in collaboration with the CAIRN project-team, we will investigate the synergy of reconfigurable computing and dynamic code generation.
Green and heterogeneous high-performance computing.
Concerning HPC systems, our approach consists in mapping, runtime managing and autotuning applications for green and heterogeneous High-Performance Computing systems up to the Exascale level. One key innovation of the proposed approach consists of introducing a separation of concerns (where self-adaptivity and energy efficient strategies are specified aside to application functionalities) promoted by the definition of a Domain Specific Language (DSL) inspired by aspect-oriented programming concepts for heterogeneous systems. The new DSL will be introduced for expressing adaptivity/energy/performance strategies and to enforce at runtime application autotuning and resource and power management. The goal is to support the parallelism, scalability and adaptability of a dynamic workload by exploiting the full system capabilities (including energy management) for emerging large-scale and extreme-scale systems, while reducing the Total Cost of Ownership (TCO) for companies and public organizations.
High-performance low-power embedded compute nodes.
We will address the design of next generation energy-efficient high-performance embedded compute nodes. It focuses at the same time on software, architecture and emerging memory and communication technologies in order to synergistically exploit their corresponding features. The approach of the project is organized around three complementary topics: 1) compilation techniques; 2) multicore architectures; 3) emerging memory and communication technologies. PACAP will focus on the compilation aspects, taking as input the software-visible characteristics of the proposed emerging technology, and making the best possible use of the new features (non-volatility, density, endurance, low-power).
Hardware Accelerated JIT Compilation.
Reconfigurable hardware offers the opportunity to limit power consumption by dynamically adjusting the number of available resources to the requirements of the running software. In particular, VLIW processors can adjust the number of available issue lanes. Unfortunately, changing the processor width often requires recompiling the application, and VLIW processors are highly dependent of the quality of the compilation, mainly because of the instruction scheduling phase performed by the compiler. Another challenge lies in the high constraints of the embedded system: the energy and execution time overhead due to the JIT compilation must be carefully kept under control.
We started exploring ways to reduce the cost of JIT compilation targeting VLIW-based heterogeneous manycore systems. Our approach lies on a hardware/software JIT compiler framework. While basic optimizations and JIT management are performed in software, the compilation back-end is implemented by means of specialized hardware. This back-end involves both instruction scheduling and register allocation, which are known to be the most time-consuming stages of such a compiler.
Security
Security is a mandatory concern of any modern computing system. Various threat models have led to a multitude of protection solutions. ALF already has contributions, thanks to the HAVEGE [62] random number generator, and code obfuscating techniques (the obfuscating just-in-time compiler [55], or thread-based control flow mangling [60]).
We plan to partner with security experts who can provide intuition, know-how and expertise, in particular in defining threat models, and assessing the quality of the solutions. Our background in compilation and architecture will help design more efficient and less expensive protection mechanisms.
We already have ongoing research directions related to security. We also plan to partner with the Inria/CentraleSupelec CIDRE project-team to design a tainting technique based on a just-in-time compiler.
Compiler-based data protection.
We will specify and design error correction codes suitable for an efficient protection of sensitive information in the context of Internet of Things (IoT) and connected objects. We will partner with experts in security and codes to prototype a platform that demonstrates resilient software. PACAP's expertise will be key to select and tune the protection mechanisms developed within the project, and to propose safe, yet cost-effective solutions from an implementation point of view.
JIT-based tainting.
Dynamic information flow control (DIFC, also known as tainting) is used used to detect intrusions and to identify vulnerabilities. It consists in attaching metadata (called taints or labels) to information containers, and to propagate the taints when particular operations are applied to the containers: reads, writes, etc. The goal is then to guarantee that confidential information is never used to generate data sent to an untrusted container; conversely, data produced by untrusted entities cannot be used to update sensitive data.
The containers can be of various granularities: fine-grain approaches can deal with single variables, coarser-grain approaches consider a file as a whole. The CIDRE project-team has developed several DIFC monitors. kBlare is coarse-grain monitor in the Linux kernel. JBlare is a fine-grain monitor for Java applications. Fine-grain monitors provide a better precision at the cost of a significant overhead in execution time.
We propose to combine the expertise of CIDRE in DIFC with our expertise in JIT compilation to design hybrid approaches. An initial static analysis of the program prior to installation or execution will feed information to a dynamic analyzer that propagates taints during just-in-time compilation.