The General Exploration Analysis Type in Intel® VTune™ Amplifier XE is used to detect microarchitectural hardware bottlenecks in an application or system. General Exploration uses hardware event counters to detect and locate issues and presents the data in a user-friendly and actionable format. This article will explain the mechanisms used in this analysis, a few best-known-methods for interpreting the results, and the various complexities and issues that arise when doing this type of analysis.
The Mechanisms behind General Exploration
The majority of the data collected and displayed in the General Exploration profile is based on hardware events collected by the Performance Monitoring Units (PMU) on the CPU. These PMU counters are hardware registers that can be programmed to count various events, for example cache misses or mispredicted branches. You can find details about the PMUs and events in the Software Developers Manual. VTune Amplifier collects the events in a mode known as Event-Based Sampling (EBS). In EBS, each PMU register is programmed to count a specific event and given a sample-after value (SAV). When the event occurs, the counter increments, and when it reaches the SAV, an interrupt fires and some data is collected, e.g. instruction pointer (IP), call stack, PID, etc... For example, if we programmed a PMU to count the L2 Cache Miss event with a SAV of 2000, on the 2000th L2 cache miss an interrupt would occur, the data would be collected, and VTune Amplifier would attribute all 2000 cache misses to the singe IP collected in the interrupt handler. Then the PMU would reset and start counting to 2000 again.
The fact that all 2000 cache misses are attributed to a single IP is the reason that this technique is called Event-Based Sampling. It is unlikely that all 2000 cache misses are actually caused by that single instruction, however, if enough samples are collected in this manner, the results should be a statistical representation of the actual behavior. When sufficient symbol information and source code is available, these instruction pointers can be resolved to specific modules, functions, and often times lines of code within an application.
There are hundreds of events that can be collected on a given microarchitecture, and these events can change from generation to generation. The General Exploration analysis is designed to harness the expert knowledge of the architects and abstract away the complexities of selecting which events to collect and present the user with understandable metrics that remain consistent across platforms. To do so, it is based on the Top-Down Analysis Method , which is briefly mentioned in next section. General Exploration collects approximately 60 events on recent microarchitectures. There are a limited number of PMU counters (usually four per logical core) so this set of events must be multiplexed in order to collect them all in a single profile. This means that four events are collected for some period of time, then they are swapped out for a different four events, and this process repeats continuously for the entire profile. The behavior of specific events must be estimated during the times when that event is not being collected. This multiplexing can introduce issues, which are described in the complexities section later in the article.
This is all done automatically when you select the General Exploration Analysis Type in VTune Amplifier.
Interpreting General Exploration Results
After running a General Exploration analysis you are presented with a summary of all the data collected. In addition to some common performance data such as elapsed time, the majority of the General Exploration data are metrics based on the hardware events described above. A metric is a combination of events into some useful value. For example, if we divide the Clockticks (CPU_CLK_UNHALTED.THREAD event) by Instructions (INST_RETIRED.ANY event), we get the commonly used metric Cycles per Instruction (CPI).
The metrics in VTune Amplifier are organized in a hierarchical fashion which is used to identify microarchitectural bottlenecks. This hierarchy and methodology is known as the Top-Down Analysis Method – a simplified, accurate and fast method to identify critical bottlenecks at the architecture and microarchitecture levels. Frequent performance bottlenecks are organized in a hierarchical structure and their cost is weighted using microarchitecture-independent metrics. As a result, the hierarchy is consistent and forward-compatible across processor generations which reduces the high learning curve traditionally required to understand new microarchitectures and their model-specific events. A detailed description of this methodology can be found in the IEEE paper . A useful white paper is also available at How to Tune Applications Using a Top-Down Characterization of Microarchitectural Issues. Additionally, there is a tuning guide for each hardware platform largely based on General Exploration available from www.intel.com/vtune-tuning-guides. After running an analysis, it’s important to focus on the hotspots within your application. These will have the largest counts for the CPU_CLK_UNHALTED.THREAD event. Next, focus on metrics that are highlighted in the GUI at the highest level of the hierarchy. Highlighted metrics have values outside a predefined threshold that represents when performance starts to be impacted negatively. Expand the highlighted metrics to see sub metrics that relate to the parent. Again look for highlighted metrics to identify performance impacts and continue expanding the highlighted metrics. Start by focusing on the lowest level of the hierarchy that has a highlighted metric. Use the hover text and documentation to understand the meaning of various metrics and how they can affect performance. The tuning guides include lots of suggestions for improving performance depending on which metrics are highlighted. If no metrics are highlighted at the lowest level, focus on the highlighted level above and try to understand what causes the performance issue indicated by that metric. After making code changes, rerun a General Exploration analysis to see if they have affected performance and how the metrics characterize the optimized code. You can use the VTune Amplifier GUI to compare results side-by-side. This performance tuning process is iterative, and can continue as long as the developer is willing to invest the time. Generally, the first fixes will have the most impact on performance, and subsequent changes will begin to have diminishing returns.
Understanding the Complexities of Event-Based Sampling
There are a number of complexities associated with EBS that aren’t necessarily prevalent in standard user-mode performance analysis. It is important to understand these issues and how they may be affecting your data as you analyze results from VTune Amplifier.
Complexity 1: Intel® Hyper-Threading Technology
One such set of complexities are introduced by the use of Intel® Hyper-Threading Technology, sometimes referred to as an implementation of simultaneous multithreading (SMT). In a system running with SMT enabled, each physical core has additional hardware which allows it to appear as two logical processors, although most of the hardware is still shared. You can imagine that designing PMUs and events for this case can be complex. Some events become less accurate when SMT is enabled because it can be difficult to distinguish the logical core responsible for the event. Additionally, calculating metrics using these events is complex because metrics make some assumptions about the underlying hardware. For example, a modern Intel CPU can allocate and retire four micro-operations (uops) per cycle. Therefore the ideal CPI would be 0.25. However, when SMT is enabled, this limit of four uops per cycle still holds true for the core, each SMT thread must share the allocate and retire resources. Thus, in an application with SMT enabled and multiple software threads consistently running, i.e. very few stalls due to memory accesses or port contention, a per-logical core (SMT core) CPI of 0.5 is the best you can do. And most applications are a mix of parallel and serial. Because of this, some metric values may vary when SMT is enabled and should be interpreted with this in mind. The main metrics to note that are affected by SMT are the top-level metrcs (categories): Retiring, Bad Speculation, Back-End Bound, and Front-End bound. The effects of SMT are proportional to amount that both SMT threads are executing simultaneously on a core and competing for the same resource, e.g. allocation slots. In the case with lots of simultaneous execution, the Retiring, Bad Speculation, and Front-End Bound metrics can be underestimated and Back-End Bound will be overestimated. In a worst-case scenario, these metrics can be off by ~1X, however the variance is generally less than that. Additionally, VTune Amplifier has thresholds built in to highlight metrics when they may indicate a performance issue, and in most cases this variance will not affect whether a metric is highlighted. It’s important to be aware of how SMT may be affecting your results, and if you suspect such effects, try running an analysis with SMT disabled in the BIOS.
Complexity 2: Event Skid
Another complexity introduced by EBS is the event skid that occurs when PMU interrupt handlers collect the Instruction Pointer (IP) that caused the event. Due to complexities in the hardware and delays in interrupt processing, the IP collected at the time of the interrupt handler which is assigned to a given event may skid to several IPs later in the execution then when the event actually occurred. In the case that metrics are being evaluated at a module or function level, this is generally not an issue because the IP will often still be in the same module or function. However, if you are looking at source lines or assembly instructions, the event counts and metrics values may skid to a line or instruction later in the binary. If metric values don’t seem to make sense, look at the few source lines before and think about whether skid may be affecting the values and whether it makes sense for the metrics to be associated with a previous line in the program. A special case of event skid can occur on branch instructions where events may get associated to branch targets which may be far away in the binary. Pay special attention to this effect if it looks like metrics associated with branch targets are actually from branch sources. Some events can be collected in a “precise” mode. These events are usually denoted with a _PS at the end of their name, for example BR_INST_RETIRED.ALL_BRANCHES_PS. These events usually have a reduced skid of just one instruction. They are attributed to the IP directly after the eventing IP - often referred to as IP+1. It is not always trivial to determine the previous IP, as in the branch case described above, so VTune Amplifier cannot do this automatically. If you’re profiling on a 4th generation Core™ processor or later, there is a fix for IP+1 issue and the eventing IP is reported. When looking at event locations for precise events on older processors, keep this IP+1 skid in mind.
Complexity 3: Event Multiplexing
The General Exploration analysis type collects 60+ events on recent processors, which requires event multiplexing, as described above. This multiplexing can cause additional variance in the collected data if runs are too short or don’t repeat any type of steady state within the time it takes to cycle through all multiplexed event groups multiple times. Obviously there is a lot of variance in the amount of time you must run your specific application to get valid representative data, but keep in mind that each group is rotated approximately every 10ms and there are ~15 groups in General Exploration. Additionally, there is a “MUX Reliability” metric in VTune Amplifier which attempts to determine how accurate any given set of metrics for a row in the grid are, with respect to multiplexing inaccuracies. This value ranges from 0 to 1, and values above 0.9 generally represent a high confidence in the metrics for that row. If you think your data may be affected by multiplexing based on the symptoms described above (low MUX Reliability values, short run durations), try running the analysis for a longer period of time or enable multiple runs in the GUI.
Complexity 4: Interrupt Handlers and Masking
One more issue to be aware of when doing EBS with VTune Amplifier is the effect interrupt handlers and masking interrupts can have on sampling. As described above, samples are counted through the use of an interrupt and interrupt handler. If the platform or application being analyzed has lots of time spent with interrupts masked, for example in other interrupt handlers, samples cannot be collected during that time. This also means that interrupt handlers themselves cannot be fully profiled by EBS. If multiple PMU interrupts arrive while interrupts are masked, only the last one will be handled when interrupts are eventually unmasked. This issue is often seen as many more samples than expected being attributed to the kernel idle loop, where interrupts are regularly masked and unmasked.
It’s important to be aware of these issues and be able to recognize when your analysis may be affected by some of these complexities.
 A. Yasin, “A Top-Down Method for Performance Analysis and Counters Architecture”, Performance Analysis of Systems and Software (ISPASS), IEEE International Symposium on, 2014