{"article":{"annotation":{"article_id":"slackswap-activation-memory-gpu-training-2026","available_at":"2026-10-13T01:40:32.731817Z","geography_revision":"ef6ee0010dda5fe7b240fd514749b43e214c57da73222d2e40ec83b276dd0977","id":"9b5ec3e857ab9c22f27a1798abbf946b759d8104d9ffc99345490e5604b94daf","input_sha256":"cf86f452065734efc0dda8dda6e009e69735a1fbf30a82f2d1e9f0ea3870744f","limitations":["Rule-derived subjects are candidates, not verified editorial labels or calibrated model probabilities.","Coverage is distinct from event location. Unknown country/city/coordinates remain absent; no automatic geocoding.","Article geometries keep their stated precision and methodology; reproducing them is not independent coordinate verification."],"method":"controlled-rules-v1","places":[{"admin1":null,"city":null,"coordinate_note":null,"coordinate_reference_system":"OGC:CRS84","coordinate_source":null,"country":"US","evidence":[{"field":"country","language":"en","note":"Explicit preserved editorial country/region; no city or national centroid inferred","source_urls":["https://arxiv.org/abs/2610.13003"]}],"geometry":null,"id":"iso3166-1:US","kind":"country-or-area","labels":{"ar":"الولايات المتحدة الأمريكية","en":"United States of America","es":"Estados Unidos de América","fr":"États-Unis d’Amérique"},"match_status":"candidate","precision":"regional","role":"coverage","uncertainty_m":null}],"rules_sha256":"fb5ad3c27c91cb840f7a0a05f7f64a5cb26ad0374409ff9457f277c7092e22b4","schema_version":1,"subjects":[{"concept_id":"medtop:13000000","confidence":null,"confidence_method":null,"evidence":[{"field":"category+topics","language":"en","note":"Preserved editorial category/topic; broad candidate only","source_urls":["https://arxiv.org/abs/2610.13003"]}],"method":"controlled-rules-v1","rule_id":"technology-broad","status":"candidate"}],"taxonomy_revision":"1f2db31d811fca07e99b16d402c10e2cc2965c9df1c7b41d9be348b40a24e7fb","taxonomy_version":"IPTC Media Topics 2026-Q2"},"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=es","category":"technology","content":{"key_points":[],"paragraphs":["Las activaciones intermedias guardadas para la retropropagación pueden superar la memoria GPU durante el entrenamiento de redes neuronales. SlackSwap perfila un paso de entrenamiento y usa como presupuesto el tiempo de transferencia que puede ocultarse durante el cálculo: transfiere dentro de ese margen las activaciones más costosas de reconstruir y recalcula las demás.","En una simulación de eventos discretos parametrizada para A100 y PCIe 4.0, con un presupuesto de memoria de activaciones igual a la mitad del pico sin límite, SlackSwap conservó el 96,3 % del rendimiento ideal para BERT-Large, el 97,6 % para GPT-2 XL y el 99,6 % para un modelo tipo Llama-7B. Los autores dicen que todos los resultados son simulados y que no modelan efectos como la fragmentación del asignador; no son mediciones de entrenamiento en GPU física."],"sections":[]},"corrections":[],"country":"US","data":{"blocks":[{"columns":[{"key":"model","label":{"ar":"النموذج","de":"Modell","en":"Model","es":"Modelo","fr":"Modèle","pt":"Modelo","tr":"Model"},"language":"en","type":"text"},{"key":"ideal_throughput","label":{"ar":"معدل الأداء مقارنة بالمثالي","de":"Durchsatz im Verhältnis zum Ideal","en":"Throughput relative to ideal","es":"Rendimiento respecto al ideal","fr":"Débit par rapport à l’idéal","pt":"Débito face ao ideal","tr":"İdeale göre verim"},"type":"number","unit":"percent"}],"description":{"ar":"النتائج ناتجة عن محاكاة وليست تشغيلًا على GPU فعلي.","de":"Die Ergebnisse stammen aus einer Simulation, nicht von einem physischen GPU-Lauf.","en":"Results are from simulation, not a run on a physical GPU.","es":"Los resultados proceden de una simulación, no de una GPU física.","fr":"Les résultats proviennent d’une simulation, pas d’un GPU physique.","pt":"Os resultados são de simulação, não de uma execução numa GPU física.","tr":"Sonuçlar gerçek GPU çalıştırması değil, benzetimdir."},"id":"simulated-training-throughput","methodology":{"ar":"تقيّم الدراسة ثلاثة أعباء عمل للمحوّلات عند ميزانية ذاكرة تنشيط قدرها 50% في محاكاة أحداث منفصلة مضبوطة وفق A100 وPCIe 4.0.","de":"Die Studie bewertet drei Transformer-Arbeitslasten bei einem Aktivierungsspeicherbudget von 50 % in einer ereignisdiskreten Simulation mit Parametern für A100 und PCIe 4.0.","en":"The study evaluates three transformer workloads at a 50% activation-memory budget in a discrete-event simulation parameterized for an A100 and PCIe 4.0.","es":"El estudio evalúa tres cargas Transformer con un presupuesto de memoria de activaciones del 50 % en una simulación de eventos discretos parametrizada para una A100 y PCIe 4.0.","fr":"L’étude évalue trois charges Transformer avec un budget mémoire d’activations de 50 % dans une simulation à événements discrets paramétrée pour un A100 et PCIe 4.0.","pt":"O estudo avalia três cargas Transformer com um orçamento de memória de ativações de 50% numa simulação de eventos discretos parametrizada para A100 e PCIe 4.0.","tr":"Çalışma, A100 ve PCIe 4.0 özelliklerini temel alan ayrık olay benzetiminde 50% etkinleştirme belleği bütçesindeki üç Transformer iş yükünü değerlendiriyor."},"rowTranslations":[{"model":{"ar":"BERT-Large","de":"BERT-Large","en":"BERT-Large","es":"BERT-Large","fr":"BERT-Large","pt":"BERT-Large","tr":"BERT-Large"}},{"model":{"ar":"GPT-2 XL","de":"GPT-2 XL","en":"GPT-2 XL","es":"GPT-2 XL","fr":"GPT-2 XL","pt":"GPT-2 XL","tr":"GPT-2 XL"}},{"model":{"ar":"نموذج على نمط Llama-7B","de":"Llama-7B-ähnlich","en":"Llama-7B-style","es":"modelo tipo Llama-7B","fr":"modèle de type Llama-7B","pt":"modelo do tipo Llama-7B","tr":"Llama-7B benzeri"}}],"rows":[{"ideal_throughput":96.3,"model":"BERT-Large"},{"ideal_throughput":97.6,"model":"GPT-2 XL"},{"ideal_throughput":99.6,"model":"Llama-7B-style"}],"sourceUrls":["https://arxiv.org/abs/2610.13003"],"title":{"ar":"معدل التدريب المحاكى","de":"Simulierter Trainingsdurchsatz","en":"Simulated training throughput","es":"Rendimiento de entrenamiento simulado","fr":"Débit d’entraînement simulé","pt":"Débito de treino simulado","tr":"Benzetilmiş eğitim verimi"},"type":"table","updatedAt":"2026-10-13T01:31:44Z"}],"version":1},"data_downloads":[{"formats":{"csv":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026/data/simulated-training-throughput?lang=es&format=csv","json":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026/data/simulated-training-throughput?lang=es&format=json"},"id":"simulated-training-throughput"}],"data_types":["table"],"editorial_illustration":{"alt":"Ilustración conceptual de tecnología, 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de UC Merced y la Universidad de Texas en Austin presentaron SlackSwap, un planificador para entrenar modelos de aprendizaje profundo. Recalcula algunas activaciones cuando se supera el presupuesto de transferencia entre GPU y memoria del host; los resultados son simulados, no mediciones de hardware.","text":"Investigadores de UC Merced y la Universidad de Texas en Austin presentaron SlackSwap, un planificador para entrenar modelos de aprendizaje profundo. Recalcula algunas activaciones cuando se supera el presupuesto de transferencia entre GPU y memoria del host; los resultados son simulados, no mediciones de hardware.\n\nLas activaciones intermedias guardadas para la retropropagación pueden superar la memoria GPU durante el entrenamiento de redes neuronales. SlackSwap perfila un paso de entrenamiento y usa como presupuesto el tiempo de transferencia que puede ocultarse durante el cálculo: transfiere dentro de ese margen las activaciones más costosas de reconstruir y recalcula las demás.\n\nEn una simulación de eventos discretos parametrizada para A100 y PCIe 4.0, con un presupuesto de memoria de activaciones igual a la mitad del pico sin límite, SlackSwap conservó el 96,3 % del rendimiento ideal para BERT-Large, el 97,6 % para GPT-2 XL y el 99,6 % para un modelo tipo Llama-7B. Los autores dicen que todos los resultados son simulados y que no modelan efectos como la fragmentación del asignador; no son mediciones de entrenamiento en GPU física.","title":"SlackSwap combina transferencias y recomputación con memoria GPU limitada","topics":[],"translations":[{"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=tr","language":"tr","url":"https://newsverum.com/tr/news/slackswap-activation-memory-gpu-training-2026"},{"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=en","language":"en","url":"https://newsverum.com/en/news/slackswap-activation-memory-gpu-training-2026"},{"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=ar","language":"ar","url":"https://newsverum.com/ar/news/slackswap-activation-memory-gpu-training-2026"},{"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=fr","language":"fr","url":"https://newsverum.com/fr/news/slackswap-activation-memory-gpu-training-2026"},{"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=es","language":"es","url":"https://newsverum.com/es/news/slackswap-activation-memory-gpu-training-2026"},{"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=de","language":"de","url":"https://newsverum.com/de/news/slackswap-activation-memory-gpu-training-2026"},{"api_url":"https://newsverum.com/api/v1/articles/slackswap-activation-memory-gpu-training-2026?lang=pt","language":"pt","url":"https://newsverum.com/pt/news/slackswap-activation-memory-gpu-training-2026"}],"url":"https://newsverum.com/es/news/slackswap-activation-memory-gpu-training-2026"},"version":"1"}