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1. Make the old-versus-new comparison fair
Before interpreting a timing difference, make the comparison as consistent as possible. Keep the model and checkpoint, inputs, precision, workload shape, batch size or serving concurrency, framework and backend, and timing boundaries the same. Define whether the measurement includes startup, compilation, data loading, or only steady-state execution.
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Record the environment for each run: accelerator model, host CPU and memory, driver, runtime, compiler and framework versions, and power or clock policy if available. Note whether profiling instrumentation is enabled, since it can affect timing. Use representative inputs and repeated runs; report a robust central result and, where useful, the spread rather than relying on one run. NVIDIA’s inference reference architecture likewise emphasizes matching the model, hardware, traffic profile, and software versions when comparing results.
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A single “inference speed” number can hide different behavior. Measure the outcomes that matter to the service: request latency, time to first token, inter-token latency, and throughput. Include startup readiness if startup is relevant, and test how results change as batch size or concurrency changes. Keep the request and input distribution, output length, cache state, and serving backend consistent. The right priority depends on whether the service is optimizing interactive response, throughput, or startup behavior.
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2. Trace the whole workload before inspecting kernels
Use the platform’s profiler to account for wall-clock time across the entire run. Look for CPU work and submission, queue gaps, host-to-device and device-to-host transfers, synchronization or waits, device execution, and idle periods. The goal is to find where elapsed time goes—not merely to identify a kernel with an impressive-looking metric.
Intel’s VTune GPU Offload analysis examines host-side offload and transfer behavior; its GPU Compute/Media Hotspots analysis can then help focus on device execution. Transfer measurements can expose an inefficient offload plan or unnecessary movement. Start with the largest contributor to total runtime, because optimizing a small or interesting kernel will not help much if most time is spent elsewhere.
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3. If the device is idle, check work size and scheduling
Low accelerator activity can mean the workload does not provide enough parallel work, the host is feeding work too slowly, or many small operations and launches are consuming time. NVIDIA’s CUDA C++ Best Practices Guide describes how insufficient work or parallelism can limit utilization. Intel’s VTune guidance also identifies scheduling overhead around smaller kernels; where the workload permits it, doing more work per invocation may reduce that overhead.
Low utilization is not, by itself, evidence of a hardware fault. It may be expected for a small workload or a latency-sensitive request. Conversely, high utilization does not prove efficient execution: use the timeline and platform-specific metrics to determine what is actually limiting progress. NVIDIA’s CUDA Programming Guide provides background on the role of parallelism and latency in GPU execution.
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4. Measure data movement and memory behavior
Record how many bytes move between host and device, how long transfers take, whether they trigger synchronization, and whether transfers overlap with computation. Avoid unnecessary movement; where the workload and memory capacity allow it, keep intermediate results on the device. Intel’s offload analysis guidance recommends minimizing transfers and retaining intermediate data on-device where possible. NVIDIA’s CUDA best-practices guidance explains why data movement can limit performance even when arithmetic is faster.
Then identify the kind of memory constraint rather than relying on one aggregate throughput figure. Bandwidth saturation, memory latency, cache behavior, a busy memory pipe, and a communication-link limit can call for different remedies. Check the metrics available for your accelerator and profiler, and interpret them in context of the timeline and workload.
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5. Inspect the dominant kernel with the right platform tools
Once the timeline shows device execution is a meaningful share of runtime, inspect the kernel or kernels that contribute most to the total. Use platform-specific profiling: tool names and metrics are not interchangeable across vendors.
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The Nsight Compute Compute Triage Guide recommends representative workloads, stable capture settings, validated metrics, and focusing on the current dominant limiter. Its central warning is: “Do not optimize from a single metric in isolation.” Re-evaluate the limiter on each GPU generation; a bottleneck on one device may not remain the bottleneck on another.
Intel GPUs
Use VTune’s GPU Offload view to investigate host offload and transfer time, then GPU hotspot and source analysis to examine critical kernels and their instruction or memory behavior. The VTune GPU Offload documentation describes this workflow.
AMD AI Engine targets
For AMD AI Engine targets covered by the AMD AI Engine guide, active utilization and memory, stream, lock, and cascade stalls can help characterize kernel behavior. These are target-specific terms and signals, not universal GPU metrics.
6. Change one thing, then measure again
Choose a change that addresses the dominant constraint shown by profiling, alter one cause at a time, and rerun the same controlled workload. Compare absolute end-to-end runtime as well as the relevant kernel duration. Keep a change only if the outcome improves for the workload’s real objective; a utilization percentage can move in an unexpected direction when total work or execution time changes.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11This sequence identifies where to investigate, not a predetermined cause. The slowdown may come from workload shape, host orchestration, transfers, memory behavior, a kernel, or differences in the software and measurement setup. The measured timeline on the new accelerator should determine which branch deserves attention.
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