Return on Investment from Academic Supercomputing: SC14 Panel

Return on Investment or ROI is a fundamental measure of effectiveness in business. It has been applied broadly across industries, including information technology and supercomputing. In this panel, we will share approaches to assessing ROI for academic supercomputing.The panel will address the challenge that “returns” from supercomputing and other computationally based research activities are often not financial. This is major distinction from other industrial sectors, where product sales, inventions, and patents might form the basis of ROI calculations. How should ROI be assessed for high performance computing in academic environments? What inroads to ROI calculations are underway by the panelists? What are challenges of ROI calculations

Reference idempotency analysis: A framework for optimizing speculative execution

Recent proposals for multithreaded architectures allow threads with unknown dependences to execute speculatively in parallel. These architectures use hardware speculative storage to buffer uncertain data, track data dependences and roll back incorrect executions. Because all memory references access the speculative storage, current proposals implement this storage using small memory structures for fast access. The limited capacity of the speculative storage causes considerable performance loss due to speculative storage overflow whenever a thread's speculative state exceeds the storage capacity. Larger threads exacerbate the over-flow problem but are preferable to smaller threads, as larger threads uncover more parallelism. In this paper, we discover a new program property called memory reference idempotency. Idempotent references need not be tracked in the speculative storage, and instead can directly access non-speculative storage (i.e., the conventional memory hierarchy). Thus, we reduce the demand fo r speculative storage space. We define a formal framework for reference idempotency and present a novel compiler-assisted speculative execution model. We prove the necessary and sufficient conditions for reference idempotency using our model. We present a compiler algorithm to label idempotent memory references for the hardware. Experimental results show that for our benchmarks, over 60% of the references in non-parallelizable program sections are idempotent

Multiplex: Unifying Conventional and Speculative Thread-Level Parallelism on a Chip Multiprocessor

Recent proposals for Chip Multiprocessors (CMPs) advocate speculative, or implicit, threading in which the hardware employs prediction to peel off instruction sequences (i.e., implicit threads) from the sequential execution stream and speculatively executes them in parallel on multiple processor cores. These proposals augment a conventional multiprocessor, which employs explicit threading, with the ability to handle implicit threads. Current proposals focus on only implicitly-threaded code sections. This paper identifies, for the first time, the issues in combining explicit and implicit threading. We present the Multiplex architecture to combine the two threading models. Multiplex exploits the similarities between implicit and explicit threading, and provides a unified support for the two threading models without additional hardware. Multiplex groups a subset of protocol states in an implicitly-threaded CMP to provide a write-invalidate protocol for explicit threads. Using a fully-integrated compiler inf rastructure for automatic generation of Multiplex code, this paper presents a detailed performance analysis for entire benchmarks, instead of just implicitly- threaded sections, as done in previous papers. We show that neither threading models alone performs consistently better than the other across the benchmarks. A CMP with four dual-issue CPUs achieves a speedup of 1.48 and 2.17 over one dual-issue CPU, using implicit-only and explicit-only threading, respectively. Multiplex matches or outperforms the better of the two threading models for every benchmark, and a four-CPU Multiplex achieves a speedup of 2.63. Our detailed analysis indicates that the dominant overheads in an implicitly-threaded CMP are speculation state overflow due to limited L1 cache capacity, and load imbalance and data dependences in fine-grain threads