Simulating all threads in a scaled GPU workload results in prohibitive simulation cost. Cycle-level simulation is orders of magnitude slower than native silicon, the only solution is to reduce the amount of work simulated while accurately representing the program.

In massively multithreaded architectures, redundantly executing the same instruction with the same operands in different threads is a significant source of inefficiency. This paper introduces Dimensionality-Aware Redundant SIMT Instruction Elimination (DARSIE), a non-speculative instruction skipping mechanism to reduce redundant operations in GPUs. DARSIE uses static markings from the compiler and information obtained at kernel launch time to skip redundant instructions before they are fetched, keeping them out of the pipeline. DARSIE exploits a new observation that there is significant redundancy across warp instructions in multi-dimensional threadblocks.

Programmable accelerators aim to provide the flexibility of traditional CPUs, with greatly improved performance and energy-efficiency. Arguably, the greatest impediment to the widespread adoption of programmable accelerators, like GPUs, is the software engineering overhead involved in getting the code to execute correctly. To help combat this issue, GPGPU computing has matured from its origins as a collection of graphics API hacks to include advanced programming features, including object-oriented programming. This level of support, in combination with a shared virtual memory space between the CPU and GPU, make it possible for rich object-oriented frameworks to execute on GPUs with little porting effort. However, executing this type of flexible code on a massively parallel accelerator introduces overhead that has not been well studied. In this poster, we analyze the direct overhead of virtual function calls on contemporary GPUs. Using the latest GPU architectures and compilers, this poster performs the analysis of how virtual function calls are implemented on GPUs. We quantify the direct overhead incurred from contemporary implementations and show that the massively multithreaded nature of GPUs creates deficiencies and opportunities not found in CPU implementations of virtual function calls.

Modern GPGPU programming extensions like OpenCL and CUDA have supported object-oriented workloads on GPUs for several generations. However, no analysis of object-oriented workloads running on massively parallel accelerators has been investigated. This extended abstract presents a performance analysis of object-oriented workloads on a PASCAL Titan X GPU. Our characterization demonstrates that GPUs have different performance trade-offs when running object-oriented code than traditional CPUs. Where CPUs are sensitive to the misprediction of indirect branches that result from virtual function calls, GPUs are more sensitive to the additional memory system pressure that comes from loading pointers and virtual function table entries.