In 2013, AmazonWeb Services revolutionized the data warehousing industry by launching Amazon Redshift, the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools. This cloud service was a significant leap from the traditional on-premise data warehousing solutions, which were expensive, not elastic, and required significant expertise to tune and operate. Customers embraced Amazon Redshift and it became the fastest growing service in AWS. Today, tens of thousands of customers use Redshift in AWS's global infrastructure to process exabytes of data daily.

For many years, the highest energy cost in processing has been data movement rather than computation, and energy is the limiting factor in processor design [21]. As the data needed for a single application grows to exabytes [56], there is clearly an opportunity to design a bandwidth-optimized architecture for big data computation by specializing hardware for data movement. We present the Data Processing Unit or DPU, a shared memory many-core that is specifically designed for high bandwidth analytics workloads. The DPU contains a unique Data Movement System (DMS), which provides hardware acceleration for data movement and partitioning operations at the memory controller that is sufficient to keep up with DDR bandwidth. The DPU also provides acceleration for core to core communication via a unique hardware RPC mechanism called the Atomic Transaction Engine. Comparison of a DPU chip fabricated in 40nm with a Xeon processor on a variety of data processing applications shows a 3× - 15× performance per watt advantage.

Specialization and accelerators are being proposed as an effective way to address the slowdown of Dennard scaling. DySER is one such accelerator, which dynamically synthesizes large compound functional units to match program regions, using a co-designed compiler and microarchitecture. We have completed a full prototype implementation of DySER integrated into the OpenSPARC processor (called SPARC-DySER), a co-designed compiler in LLVM, and a detailed performance evaluation on an FPGA system, which runs an Ubuntu Linux distribution and full applications. Through the prototype, this paper evaluates the fundamental principles of DySER acceleration. Our two key findings are: i) the DySER execution model and microarchitecture provides energy efficient speedups and the integration of DySER does not introduce overheads – overall, DySER’s performance improvement to OpenSPARC is 6X, consuming only 200mW ; ii) on the compiler side, the DySER compiler is effective at extracting computationally intensive regular and irregular code. For non-computationally intense irregular code, two control flow shapes curtail the compiler’s effectiveness, and we identify potential adaptive mechanisms. Finally, our experience of bringing up an end-to-end prototype of an ISA-exposed accelerator has made clear that two particular artifacts are greatly needed to perform this type of design more quickly and effectively: 1) Open-source implementations of high-performance baseline processors, and 2) Declarative tools for quickly specifying combinations of known compiler transforms.

Modern microprocessors exploit data level parallelism through in-core data-parallel accelerators in the form of short vector ISA extentions such as SSE/AVX and NEON. Although these ISA extentions have existed for decades, compilers do not generate good quality, high-performance vectorized code without significant programmer intervention and manual optimization. The fundamental problem is that the architecture is too rigid, which overly complicates the compiler's role and simultaneously restricts the types of codes that the compiler can profitably map to these data-parallel accelerators. We take a fundamentally new approach that first makes the architecture more flexible and exposes this flexibility to the compiler. Counter-intuitively, increasing the complexity of the accelerator's interface to the compiler enables a more robust and efficient system that supports many types of codes. This system also enables the performance of auto-acceleration to be comparable to that of manually-optimized implementations. To address the challenges of compiling for flexible accelerators, we propose a variant of Program Dependence Graph called the Access Execute Program Dependence Graph to capture spatio-temporal aspects of memory accesses and computations. We implement a compiler that uses this representation and evaluate it by considering both a suite of kernels developed and tuned for SSE, and “challenge” data-parallel applications, the Parboil benchmarks. We show that our compiler, which targets the DySER accelerator, provides high-quality code for the kernels and full applications, commonly reaching within 30% of manually-optimized and out-performs compiler-produced SSE code by 1.8×.

Accelerators and specialization in various forms are emerging as a way to increase processor performance. Examples include Navigo, Conservation-Cores, BERET, and DySER. While each of these employ different primitives and principles to achieve specialization, they share some common concerns with regards to implementation. Two of these concerns are: how to integrate them with a commercial processor and how to develop their compiler toolchain. This paper undertakes an implementation study of one design point: integration of DySER into OpenSPARC, a design we call OpenSPlySER. We report on our implementation exercise and quantitative results, and conclude with a set of our lessons learned. We demonstrate that DySER delivers on its goal of providing a non-intrusive accelerator design. OpenSPlySERruns on an Virtex-5 FPGA, boots unmodified Linux, and runs most of the SPECINT benchmarks with our compiler. Due to physical design constraints, speedups on full benchmarks are modest for the FPGA prototype. On targeted microbenchmarks, OpenSPlySER delivers up to a 31-fold speedup over the baseline OpenSPARC. We conclude with some lessons learned from this somewhat unique exercise of significantly modifying a commercial processor. To the best of our knowledge, this work is one of the most ambitious extensions of OpenSPARC.

Due to limits in technology scaling, energy efficiency of logic devices is decreasing in successive generations. To provide continued performance improvements without increasing power, regardless of the sequential or parallel nature of the application, microarchitectural energy efficiency must improve. We propose Dynamically Specialized Datapaths to improve the energy efficiency of general purpose programmable processors. The key insights of this work are the following. First, applications execute in phases and these phases can be determined by creating a path-tree of basic-blocks rooted at the inner-most loop. Second, specialized datapaths corresponding to these path-trees, which we refer to as DySER blocks, can be constructed by interconnecting a set of heterogeneous computation units with a circuit-switched network. These blocks can be easily integrated with a processor pipeline. A synthesized RTL implementation using an industry 55nm technology library shows a 64-functional-unit DySER block occupies approximately the same area as a 64 KB single-ported SRAM and can execute at 2 GHz. We extend the GCC compiler to identify path-trees and code-mapping to DySER and evaluate the PAR-SEC, SPEC and Parboil benchmarks suites. Our results show that in most cases two DySER blocks can achieve the same performance (within 5%) as having a specialized hardware module for each path-tree. A 64-FU DySER block can cover 12% to 100% of the dynamically executed instruction stream. When integrated with a dual-issue out-of-order processor, two DySER blocks provide geometric mean speedup of 2.1X (1.15X to 10X), and geometric mean energy reduction of 40% (up to 70%), and 60% energy reduction if no performance improvement is required.

With technology scaling, manufacture-time and in-field permanent faults are becoming a fundamental problem. Multi-core architectures with spares can tolerate them by detecting and isolating faulty cores, but the required fault detection coverage becomes effectively 100% as the number of permanent faults increases. Dual-modular redundancy(DMR) can provide 100% coverage without assuming device-level fault models, but its overhead is excessive.

Significant improvement to visual quality for real-time 3D graphics requires modeling of complex illumination effects like soft-shadows, reflections, and diffuse lighting interactions. The conventional Z-buffer algorithm driven GPU model does not provide sufficient support for this improvement. This paper targets the entire graphics system stack and demonstrates algorithms, a software architecture, and a hardware architecture for real-time rendering with a paradigm shift to ray-tracing. The three unique features of our system called Copernicus are support for dynamic scenes, high image quality, and execution on programmable multicore architectures. The focus of this paper is the synergy and interaction between applications, architecture, and evaluation. First, we describe the ray-tracing algorithms which are designed to use redundancy and partitioning to achieve locality. Second, we describe the architecture which uses ISA specialization, multi-threading to hide memory delays and supports only local coherence. Finally, we develop an analytical performance model for our 128-core system, using measurements from simulation and a scaled-down prototype system. More generally, this paper addresses an important issue of mechanisms and evaluation for challenging workloads for future processors. Our results show that a single 8-core tile (each core 4-way multithreaded) can be almost 100% utilized and sustain 10 million rays/second. Sixteen such tiles, which can fit on a 240 mm2chip in 22 nm technology, make up the system and with our anticipated improvements in algorithms, can sustain real-time rendering. The mechanisms and the architecture can potentially support other domains like irregular scientific computations and physics computations.