ISA-independent workload characterization and its implications for specialized architectures

Abstract

Abstract-Specialized architectures will become increasingly important as the computing industry demands more energyefficient designs. The application-centric design style for these architectures is heavily dependent on workload characterization of intrinsic program characteristics, but at the same time these architectures are likely to be decoupled from legacy ISAs. In this work, we perform ISA-independent workload characterization for a variety of important intrinsic program characteristics relating to computation, memory, and control flow. The analysis is performed using a JIT compiler that emits ISA-independent instructions. We compare this analysis with an x86 trace and find that several of the analyses are highly sensitive to the ISA. We conclude that designers of specialized architectures must adopt ISA-independent workload characterization approaches.

Increasing the Discriminatory Power of DEA Using Shannon’s Entropy

“…This concept has been extended to different scientific fields, such as physics, social sciences, and so on (e.g., [25][26][27][28], etc.). To the best of our knowledge, the first work integrating Shannon's entropy and DEA is [17], in which the authors integrated a series of efficiency scores of a DMU based upon many different DEA models (such as CCR, BCC and so on) into a comprehensive efficiency score via using Shannon's entropy to calculate the degree of importance of each model.…”

Aladdin: A pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures

“…Optimistic IR Aladdin builds the DDDG from a dynamic instruction trace, where the choice of the ISA significantly impacts the complexity and granularity of the nodes in the graph. In fact, a trace using a machine-specific ISA contains instructions that are not part of the program but produced due to the artifacts of the ISA [49], e.g., register spills. To avoid such artifacts, Aladdin uses a high-level, machineindependent intermediate representation (IR) provided by the ILDJIT compiler [10].…”

Memory system characterization of big data workloads

“…Barroso et al [9] characterize the memory references of various commercial workloads. Domain specific characterization studies include memory characterization of parallel data mining workloads [15], of the ECperf benchmark [16], of memcached [17], and of the SPEC CPU2000 and CPU2006 benchmark suites [10] [11] [12]. Particularly noteworthy is the work of Shao et al [12], where statistical measures are used for memory characterization.…”

“…This emphasis becomes even more important with recent trends in emerging memory technologies [7] [8], that are expected to offer different characteristics from conventional DRAMsuch as higher latencies, differing capacities, persistence, etc. In order to have software run efficiently using such technologies, it becomes critical to characterize and understand the memory usages.While various studies have been performed on memory characterization of workloads [9] [10] [11] [12] over the past decade, unfortunately, most of them focus on the SPEC benchmark suites and traditional computing models. Very few studies have examined memory behaviors of big data workloads-and these are mostly specific to an optimization, such as TLB improvements [13] [14].…”