Software-managed Memory Research Articles

SoMMA: A software-managed memory architecture for multi-issue processors

Embedded processors rely on the efficient use of instruction-level parallelism to answer the performance and energy needs of modern applications. Though improving performance is the primary goal for processors in general, it might lead to a negative impact on energy consumption, a particularly critical constraint for current systems. In this paper, we present SoMMA, a software-managed memory architecture for embedded multi-issue processors that can reduce energy consumption and energy-delay product (EDP), while still providing an increase in memory bandwidth. We combine the use of software-managed memories (SMM) with the data cache, and leverage the lower energy access cost of SMMs to provide a processor with reduced energy consumption and EDP. SoMMA also provides a better overall performance, as memory accesses can be performed in parallel, with no cost in extra memory ports. Compiler-automated code transformations minimize the programmer's effort to benefit from the proposed architecture. The approach shows average speedups of 1.118x and 1.121x, while consuming up to 11% and 12.8% less energy when comparing two modified ρVEX processors and their baselines, at full-system level comparisons. SoMMA also shows reduction of up to 41.5% on full-system EDP, maintaining the same processor area as baseline processors.

Accelerating the discontinuous Galerkin cell-vertex scheme solver on GPU-powered systems

The discontinuous Galerkin cell-vertex scheme (DG-CVS) is a high-order space-time Riemann-solver-free numerical solver for general hyperbolic conservation laws. It fuses the discontinuous Galerkin (DG) method and the conservation element/solution element (CE/SE) method to take advantage of the best features of both methods. In DG-CVS, the time derivatives of the solution are treated as independent unknowns together with spatial derivatives of the solution, which is amendable to a GPU's parallel execution style. In a GPU environment, this type of scientific application poses challenges, such as high thread divergence, low kernel occupancy, and hardware-unfriendly memory access patterns. This paper presents various optimisations that address those issues. Our proposed optimisations include thread remapping, register pressure reduction as well as software-managed cache memory utilisation. DG-CVS is accelerated by up to 54% on AMD HD7970 GPU, when compared to CPU only execution.

Acyclic orientation graph coloring for software-managed memory allocation

This paper presents a novel compiler algorithm, called acyclic orientation graph coloring (AOG coloring), for managing data objects in software-managed memory allocation. The key insight is that softwaremanaged memory allocation could be solved as an interval coloring problem, or equivalently, an acyclic orientation problem. We generalize graph coloring register allocation to interval coloring memory allocation by maintaining an acyclic orientation to the currently colored subgraph. This is achieved with some well-crafted heuristics, including Aggressive Simplify that does not necessarily preserve colorability and Best-Fit Select that assigns intervals (i.e., colors) to nodes by possibly adjusting the colors already assigned to other nodes earlier. Our algorithm generalizes and subsumes as a special case the classical graph coloring register allocation algorithm without notably increased complexity: it deals with memory allocation while preserving the elegance and practicality of traditional graph coloring register allocation. We have implemented our algorithm and tested it on Appel’s 27921 interference graphs for scalars (augmented with node weights). Our algorithm outperforms Memory Coloring, the best in the literature, for software-managed memory allocation, on 98.64% graphs, in which, the gaps are more than 20% on 68.31% graphs and worse only on 0.29% graphs. We also tested it on all the 73 DIMACS weighted benchmarks (weighted graphs), AOG Coloring outperforms Memory Coloring on all of the benchmarks, in which, the gaps are more than 20% on 83.56% graphs.