Uppsala Architecture Research Team

Fast Modeling
Our modeling technologies allow us to rapidly explore different architectural parameters and analyze application behavior with very low-overhead. We can thereby take advantage of our unique measurement capabilities to rapidly and accurately predict behavior across a wide range of hardware and software configurations.
Statistical Cache Modeling
Statistical cache modeling is an extremely fast method for predicting an application's cache miss ratio from cheap (20% overhead) sampled memory reuse data. With these models we can instantly predict cache miss ratios for arbitrary sized caches. |
Profile-based Contention Modeling
Leveraging the profile data collected from our Cache Pirate measurement tool, we can rapidly model cache contention effects, including all the details of the actual hardware and application. |
High Performance Simulation
Our high-performance simulation frameworks allow us to understand and analyze full-system behavior of realistic (large) workloads. By exploiting hardware virtualization and performance sampling, our methods can reach execution rates comparable to native execution. |
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Our new graphics tracing and replay framework allows us to explore system-level effects on heterogeneous CPU+GPU memory systems. By efficiently generating GPU memory access traces for modern graphics applications, GLTraceSim replays them through high-level models and detailed simulators to explore effects in bandwidth, cache misses, scheduling and performance. GLTraceSim is built upon well-maintained, publicly-available tools. |