## Horizontal Bar Chart: Performance Comparison Across CPU Architectures and Instruction Sets
### Overview
This image is a horizontal bar chart comparing computational performance, measured in Giga Updates Per Second (GUP/s), across three different CPU microarchitectures (IvyBridge-EP, Haswell, KNC). For each architecture, performance is shown for different instruction sets, comparing a "Scalar" (black bar) baseline to a "Vector" (yellow bar) implementation. The percentage improvement of the vector version over the scalar version is explicitly labeled for each pair.
### Components/Axes
* **Chart Type:** Horizontal grouped bar chart.
* **X-Axis (Top):** Labeled "Performance [GUP/s]". The scale runs from 0 to 0.5, with major tick marks at 0, 0.1, 0.2, 0.3, 0.4, and 0.5.
* **Y-Axis (Left):** Lists three CPU microarchitecture groups, separated by horizontal lines. Within each group, specific instruction set extensions are listed.
* **Legend:** Located in the top-right corner of the chart area.
* **Black Bar:** Labeled "Scalar".
* **Yellow Bar:** Labeled "Vector".
* **Data Labels:** Each yellow "Vector" bar has a text label indicating the percentage improvement (e.g., "+27%") relative to its paired black "Scalar" bar.
### Detailed Analysis
The chart is segmented into three distinct regions from top to bottom:
**1. IvyBridge-EP Architecture (Top Section)**
* **Scalar (Black):** Performance is approximately 0.12 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.15 GUP/s.
* **Improvement:** Labeled as **+27%**.
* **Scalar (Black):** Performance is approximately 0.22 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.27 GUP/s.
* **Improvement:** Labeled as **+22%**.
* **AVX:**
* **Scalar (Black):** Performance is approximately 0.28 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.38 GUP/s.
* **Improvement:** Labeled as **+37%**.
**2. Haswell Architecture (Middle Section)**
* **Scalar (Black):** Performance is approximately 0.14 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.15 GUP/s.
* **Improvement:** Labeled as **+7%**.
* **SSE:**
* **Scalar (Black):** Performance is approximately 0.35 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.40 GUP/s.
* **Improvement:** Labeled as **+13%**.
* **AVX:**
* **Scalar (Black):** Performance is approximately 0.35 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.50 GUP/s (the bar extends slightly past the 0.5 mark).
* **Improvement:** Labeled as **+44%**.
* **AVX/FMA3:**
* **Scalar (Black):** Performance is approximately 0.35 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.50 GUP/s (the bar extends slightly past the 0.5 mark).
* **Improvement:** Labeled as **+44%**.
* **AVX2/FMA3:**
* **Scalar (Black):** Performance is approximately 0.35 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.46 GUP/s.
* **Improvement:** Labeled as **+31%**.
**3. KNC (Knights Corner) Architecture (Bottom Section)**
* **Scalar (Black):** Performance is very low, approximately 0.02 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.07 GUP/s.
* **Improvement:** Labeled as **+126%**.
* **IMCI:**
* **Scalar (Black):** Performance is approximately 0.08 GUP/s.
* **Vector (Yellow):** Performance is approximately 0.21 GUP/s.
* **Improvement:** Labeled as **+160%**.
### Key Observations
1. **Universal Vectorization Benefit:** In every single case, the yellow "Vector" bar is longer than its paired black "Scalar" bar, demonstrating that vectorization improves performance for this workload across all tested architectures and instruction sets.
2. **Magnitude of Improvement Varies:** The performance gain from vectorization is not uniform. It ranges from a modest **+7%** (Haswell, Scalar vs. Vector) to a very substantial **+160%** (KNC, IMCI).
3. **Architecture Performance Tiers:** The Haswell architecture, particularly with AVX and FMA3 instructions, achieves the highest absolute performance, reaching or exceeding 0.5 GUP/s. IvyBridge-EP shows moderate performance, while the KNC architecture shows the lowest absolute performance but the highest relative gains from vectorization.
4. **Instruction Set Impact:** Within Haswell, moving from SSE to AVX/AVX2 with FMA3 shows a clear performance jump for the vectorized code. The Scalar performance for AVX, AVX/FMA3, and AVX2/FMA3 appears similar (~0.35 GUP/s), suggesting the scalar bottleneck is elsewhere.
5. **KNC's Unique Profile:** The KNC (a many-core Xeon Phi architecture) shows dramatically different behavior. Its scalar performance is extremely low, but vectorization (especially with IMCI) unlocks massive relative gains, highlighting its design as a vector-oriented processor.
### Interpretation
This chart provides a clear technical demonstration of the performance impact of **vectorization** (using SIMD instructions) on a specific computational workload (measured in GUP/s). The data suggests:
* **Vectorization is a critical optimization:** For this workload, failing to use vector instructions leaves significant performance on the table, especially on architectures designed for it like KNC.
* **Architectural design dictates optimization payoff:** The benefit of vectorization is highly dependent on the underlying CPU microarchitecture. A modern core like Haswell sees good gains (+44%), but a many-core, vector-centric design like KNC sees transformative gains (+160%), as its scalar units are likely a severe bottleneck.
* **Instruction set evolution matters:** The progression from SSE to AVX to AVX2/FMA3 on Haswell shows increasing peak vector performance, indicating that newer, more capable instruction sets are essential for extracting maximum performance from the hardware.
* **The "why" behind the numbers:** The low scalar performance on KNC is likely because its cores are simplified and heavily reliant on wide vector units for throughput. The high vector gains confirm that the workload is well-suited to parallel data processing. The similar scalar performance across Haswell's AVX variants suggests the scalar code path is not utilizing the advanced features of FMA or AVX2, hitting a different bottleneck.
In essence, the chart is a compelling case study for performance engineers: to achieve high throughput (GUP/s), one must not only use vectorization but also select the appropriate architecture and instruction set for the target workload. The KNC data, in particular, acts as a stark warning about the performance penalty of running non-vectorized code on vector-optimized hardware.
