Memory and Parallelism Analysis Using a Platform-Independent Approach

Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this ongoing work, we extend the state-of-the-art platform-independent software analysis tool with NMC related metrics such as memory entropy, spatial locality, data-level, and basic-block-level parallelism. These metrics help to identify the applications more suitable for NMC architectures.Comment: 22nd ACM International Workshop on Software and Compilers for Embedded Systems (SCOPES '19), May 201

Static locality analysis for cache management

Most memory references in numerical codes correspond to array references whose indices are affine functions of surrounding loop indices. These array references follow a regular predictable memory pattern that can be analysed at compile time. This analysis can provide valuable information like the locality exhibited by the program, which can be used to implement more intelligent caching strategy. In this paper we propose a static locality analysis oriented to the management of data caches. We show that previous proposals on locality analysis are not appropriate when the proposals have a high conflict miss ratio. This paper examines those proposals by introducing a compile-time interference analysis that significantly improve the performance of them. We first show how this analysis can be used to characterize the dynamic locality properties of numerical codes. This evaluation show for instance that a large percentage of references exhibit any type of locality. This motivates the use of a dual data cache, which has a module specialized to exploit temporal locality, and a selective cache respectively. Then, the performance provided by these two cache organizations is evaluated. In both organizations, the static locality analysis is responsible for tagging each memory instruction accordingly to the particular type(s) of locality that it exhibits.Peer ReviewedPostprint (published version

Three-dimensional memory vectorization for high bandwidth media memory systems

Vector processors have good performance, cost and adaptability when targeting multimedia applications. However, for a significant number of media programs, conventional memory configurations fail to deliver enough memory references per cycle to feed the SIMD functional units. This paper addresses the problem of the memory bandwidth. We propose a novel mechanism suitable for 2-dimensional vector architectures and targeted at providing high effective bandwidth for SIMD memory instructions. The basis of this mechanism is the extension of the scope of vectorization at the memory level, so that 3-dimensional memory patterns can be fetched into a second-level register file. By fetching long blocks of data and by reusing 2-dimensional memory streams at this second-level register file, we obtain a significant increase in the effective memory bandwidth. As side benefits, the new 3-dimensional load instructions provide a high robustness to memory latency and a significant reduction of the cache activity, thus reducing power and energy requirements. At the investment of a 50% more area than a regular SIMD register file, we have measured and average speed-up of 13% and the potential for power savings in the L2 cache of a 30%.Peer ReviewedPostprint (published version

Trace-level reuse

Trace-level reuse is based on the observation that some traces (dynamic sequences of instructions) are frequently repeated during the execution of a program, and in many cases, the instructions that make up such traces have the same source operand values. The execution of such traces will obviously produce the same outcome and thus, their execution can be skipped if the processor records the outcome of previous executions. This paper presents an analysis of the performance potential of trace-level reuse and discusses a preliminary realistic implementation. Like instruction-level reuse, trace-level reuse can improve performance by decreasing resource contention and the latency of some instructions. However, we show that trace-level reuse is more effective than instruction-level reuse because the former can avoid fetching the instructions of reused traces. This has two important benefits: it reduces the fetch bandwidth requirements, and it increases the effective instruction window size since these instructions do not occupy window entries. Moreover, trace-level reuse can compute all at once the result of a chain of dependent instructions, which may allow the processor to avoid the serialization caused by data dependences and thus, to potentially exceed the dataflow limit.Peer ReviewedPostprint (published version